├── .gitignore ├── .travis.yml ├── CellProfiler3Pipelines ├── ExampleColocalization.cppipe ├── ExampleCometAssay.cppipe ├── ExampleFly.cppipe ├── ExampleFlyURL.cppipe ├── ExampleHuman.cppipe ├── ExampleIlluminationCorrection_Example1_AllMethod.cppipe ├── ExampleIlluminationCorrection_Example1_EachMethod.cppipe ├── ExampleIlluminationCorrection_Example2.cppipe ├── ExampleIlluminationCorrection_Example3.cppipe ├── ExampleImagingFlowCytometryObjectsInGrid.cppipe ├── ExampleNeighbors.cppipe ├── ExamplePercentPositive.cppipe ├── ExampleSpeckles.cppipe ├── ExampleTrackObjects.cppipe ├── ExampleTumor.cppipe ├── ExampleUntangleAndStraightenWorms.cppipe ├── ExampleUntangleWorms.cppipe ├── ExampleUntangleWormsBrightField.cppipe ├── ExampleVitra.cppipe ├── ExampleWoundHealing.cppipe ├── ExampleYeastColonies.cppipe └── ExampleYeastPatches.cppipe ├── ExampleColocalization ├── ExampleColocalization.cppipe ├── README.md └── images │ ├── 0_1_N_G.png │ ├── 0_1_N_R.png │ ├── 0_2_N_G.png │ └── 0_2_N_R.png ├── ExampleCometAssay ├── ExampleCometAssay.cppipe ├── README.md └── images │ ├── CometTails.tif │ └── NoTails.tif ├── ExampleFly ├── ExampleFly.cppipe ├── ExampleFly.csv └── images │ ├── 01_POS002_D.TIF │ ├── 01_POS002_F.TIF │ ├── 01_POS002_R.TIF │ ├── 01_POS076_D.TIF │ ├── 01_POS076_F.TIF │ ├── 01_POS076_R.TIF │ ├── 01_POS218_D.TIF │ ├── 01_POS218_F.TIF │ └── 01_POS218_R.TIF ├── ExampleHuman ├── ExampleHuman.cppipe ├── README.md └── images │ ├── AS_09125_050116030001_D03f00d0.tif │ ├── AS_09125_050116030001_D03f00d1.tif │ └── AS_09125_050116030001_D03f00d2.tif ├── ExampleIlluminationCorrection ├── ExampleIlluminationCorrection_Example1_AllMethod.cppipe ├── ExampleIlluminationCorrection_Example1_EachMethod.cppipe ├── ExampleIlluminationCorrection_Example2.cppipe ├── ExampleIlluminationCorrection_Example3.cppipe ├── ExampleIlluminationCorrection_Tutorial.pdf ├── README.md └── images │ ├── --W00001--P00001--Z00000--T00000--cherry.tif │ ├── ADSAStaphInfection2_A01_w2247376DD-6ADD-442D-AE47-F54A05F3EA94.tif │ ├── AS_09047_050428030001_O01f00d2.TIF │ ├── AS_09047_050428030001_O01f01d2.TIF │ ├── AS_09047_050428030001_O01f02d2.TIF │ ├── AS_09047_050428030001_O02f00d2.TIF │ ├── AS_09047_050428030001_O02f01d2.TIF │ ├── AS_09047_050428030001_O02f02d2.TIF │ ├── AS_09047_050428030001_O03f00d2.TIF │ ├── AS_09047_050428030001_O03f01d2.TIF │ ├── AS_09047_050428030001_O03f02d2.TIF │ ├── AS_09047_050428030001_O04f00d2.TIF │ ├── AS_09047_050428030001_O04f01d2.TIF │ ├── AS_09047_050428030001_O04f02d2.TIF │ ├── AS_09047_050428030001_O05f00d2.TIF │ ├── AS_09047_050428030001_O05f01d2.TIF │ ├── AS_09047_050428030001_O05f02d2.TIF │ ├── AS_09047_050428030001_O06f00d2.TIF │ ├── AS_09047_050428030001_O06f01d2.TIF │ ├── AS_09047_050428030001_O06f02d2.TIF │ ├── AS_09047_050428030001_O07f00d2.TIF │ ├── AS_09047_050428030001_O07f01d2.TIF │ ├── AS_09047_050428030001_O07f02d2.TIF │ ├── AS_09047_050428030001_O08f00d2.TIF │ ├── AS_09047_050428030001_O08f01d2.TIF │ ├── AS_09047_050428030001_O08f02d2.TIF │ ├── AS_09047_050428030001_O09f00d2.TIF │ ├── AS_09047_050428030001_O09f01d2.TIF │ ├── AS_09047_050428030001_O09f02d2.TIF │ ├── AS_09047_050428030001_O10f00d2.TIF │ ├── AS_09047_050428030001_O10f01d2.TIF │ ├── AS_09047_050428030001_O10f02d2.TIF │ ├── AS_09047_050428030001_O11f00d2.TIF │ ├── AS_09047_050428030001_O11f01d2.TIF │ ├── AS_09047_050428030001_O11f02d2.TIF │ ├── AS_09047_050428030001_O12f00d2.TIF │ ├── AS_09047_050428030001_O12f01d2.TIF │ ├── AS_09047_050428030001_O12f02d2.TIF │ ├── AS_09047_050428030001_O13f00d2.TIF │ ├── AS_09047_050428030001_O13f01d2.TIF │ ├── AS_09047_050428030001_O13f02d2.TIF │ ├── AS_09047_050428030001_O14f00d2.TIF │ ├── AS_09047_050428030001_O14f01d2.TIF │ ├── AS_09047_050428030001_O14f02d2.TIF │ ├── AS_09047_050428030001_O15f00d2.TIF │ ├── AS_09047_050428030001_O15f01d2.TIF │ ├── AS_09047_050428030001_O15f02d2.TIF │ ├── AS_09047_050428030001_O16f00d2.TIF │ ├── AS_09047_050428030001_O16f01d2.TIF │ ├── AS_09047_050428030001_O16f02d2.TIF │ ├── AS_09047_050428030001_O17f00d2.TIF │ ├── AS_09047_050428030001_O17f01d2.TIF │ ├── AS_09047_050428030001_O17f02d2.TIF │ ├── AS_09047_050428030001_O18f00d2.TIF │ ├── AS_09047_050428030001_O18f01d2.TIF │ ├── AS_09047_050428030001_O18f02d2.TIF │ ├── AS_09047_050428030001_O19f00d2.TIF │ ├── AS_09047_050428030001_O19f01d2.TIF │ ├── AS_09047_050428030001_O19f02d2.TIF │ ├── AS_09047_050428030001_O20f00d2.TIF │ ├── AS_09047_050428030001_O20f01d2.TIF │ ├── AS_09047_050428030001_O20f02d2.TIF │ ├── AS_09047_050428030001_O21f00d2.TIF │ ├── AS_09047_050428030001_O21f01d2.TIF │ ├── AS_09047_050428030001_O21f02d2.TIF │ ├── AS_09047_050428030001_O22f00d2.TIF │ ├── AS_09047_050428030001_O22f01d2.TIF │ ├── AS_09047_050428030001_O22f02d2.TIF │ ├── AS_09047_050428030001_O23f00d2.TIF │ ├── AS_09047_050428030001_O23f01d2.TIF │ ├── AS_09047_050428030001_O23f02d2.TIF │ ├── AS_09047_050428030001_O24f00d2.TIF │ ├── AS_09047_050428030001_O24f01d2.TIF │ └── AS_09047_050428030001_O24f02d2.TIF ├── ExampleImagingFlowCytometryObjectsInGrid ├── ExampleImagingFlowCytometryObjectsInGrid.cppipe ├── README.md └── images │ ├── Ch1_1.tif │ ├── Ch1_2.tif │ ├── Ch6_1.tif │ ├── Ch6_2.tif │ ├── Ch7_1.tif │ └── Ch7_2.tif ├── ExampleNeighbors ├── ExampleNeighbors.cppipe ├── README.md └── images │ └── Clones1.JPG ├── ExamplePercentPositive ├── ExamplePercentPositive.cppipe ├── README.md └── images │ ├── AS_09125_050116030001_D03f00d0.tif │ └── AS_09125_050116030001_D03f00d1.tif ├── ExampleSpeckles ├── ExampleSpeckles.cppipe ├── README.md └── images │ ├── 1-162hrh2ax2.tif │ └── 1-162hrhoe2.tif ├── ExampleStraightenWorms ├── ExampleUntangleAndStraightenWorms.cppipe ├── README.md └── images │ ├── 101210OranePlt1_C16_w1_[9AA4D9EA-18E4-4354-8C7D-0202029A8048].tif │ ├── 101210OranePlt1_C16_w2_[EFB0F53A-F40F-46D8-A61A-51C2BE61E460].tif │ ├── 101210OranePlt1_C16_w3_[E3E9B145-D9B7-49E1-8159-496F065708F7].tif │ └── WormModel.xml ├── ExampleTrackObjects ├── ExampleTrackObjects.cppipe ├── README.md └── images │ ├── Sequence1 │ ├── DrosophilaEmbryo_GFPHistone_0000.tif │ ├── DrosophilaEmbryo_GFPHistone_0001.tif │ ├── DrosophilaEmbryo_GFPHistone_0002.tif │ ├── DrosophilaEmbryo_GFPHistone_0003.tif │ ├── DrosophilaEmbryo_GFPHistone_0004.tif │ ├── DrosophilaEmbryo_GFPHistone_0005.tif │ ├── DrosophilaEmbryo_GFPHistone_0006.tif │ ├── DrosophilaEmbryo_GFPHistone_0007.tif │ ├── DrosophilaEmbryo_GFPHistone_0008.tif │ ├── DrosophilaEmbryo_GFPHistone_0009.tif │ ├── DrosophilaEmbryo_GFPHistone_0010.tif │ ├── DrosophilaEmbryo_GFPHistone_0011.tif │ ├── DrosophilaEmbryo_GFPHistone_0012.tif │ ├── DrosophilaEmbryo_GFPHistone_0013.tif │ ├── DrosophilaEmbryo_GFPHistone_0014.tif │ ├── DrosophilaEmbryo_GFPHistone_0015.tif │ ├── DrosophilaEmbryo_GFPHistone_0016.tif │ ├── DrosophilaEmbryo_GFPHistone_0017.tif │ ├── DrosophilaEmbryo_GFPHistone_0018.tif │ ├── DrosophilaEmbryo_GFPHistone_0019.tif │ └── DrosophilaEmbryo_GFPHistone_0020.tif │ ├── Sequence2 │ ├── DrosophilaEmbryo_GFPHistone_0000.tif │ ├── DrosophilaEmbryo_GFPHistone_0001.tif │ ├── DrosophilaEmbryo_GFPHistone_0002.tif │ ├── DrosophilaEmbryo_GFPHistone_0003.tif │ ├── DrosophilaEmbryo_GFPHistone_0004.tif │ ├── DrosophilaEmbryo_GFPHistone_0005.tif │ ├── DrosophilaEmbryo_GFPHistone_0006.tif │ ├── DrosophilaEmbryo_GFPHistone_0007.tif │ ├── DrosophilaEmbryo_GFPHistone_0008.tif │ ├── DrosophilaEmbryo_GFPHistone_0009.tif │ ├── DrosophilaEmbryo_GFPHistone_0010.tif │ ├── DrosophilaEmbryo_GFPHistone_0011.tif │ ├── DrosophilaEmbryo_GFPHistone_0012.tif │ ├── DrosophilaEmbryo_GFPHistone_0013.tif │ ├── DrosophilaEmbryo_GFPHistone_0014.tif │ ├── DrosophilaEmbryo_GFPHistone_0015.tif │ ├── DrosophilaEmbryo_GFPHistone_0016.tif │ ├── DrosophilaEmbryo_GFPHistone_0017.tif │ ├── DrosophilaEmbryo_GFPHistone_0018.tif │ ├── DrosophilaEmbryo_GFPHistone_0019.tif │ └── DrosophilaEmbryo_GFPHistone_0020.tif │ └── Sequence3 │ ├── DrosophilaEmbryo_GFPHistone_0000.tif │ ├── DrosophilaEmbryo_GFPHistone_0001.tif │ ├── DrosophilaEmbryo_GFPHistone_0002.tif │ ├── DrosophilaEmbryo_GFPHistone_0003.tif │ ├── DrosophilaEmbryo_GFPHistone_0004.tif │ ├── DrosophilaEmbryo_GFPHistone_0005.tif │ ├── DrosophilaEmbryo_GFPHistone_0006.tif │ ├── DrosophilaEmbryo_GFPHistone_0007.tif │ ├── DrosophilaEmbryo_GFPHistone_0008.tif │ ├── DrosophilaEmbryo_GFPHistone_0009.tif │ ├── DrosophilaEmbryo_GFPHistone_0010.tif │ ├── DrosophilaEmbryo_GFPHistone_0011.tif │ ├── DrosophilaEmbryo_GFPHistone_0012.tif │ ├── DrosophilaEmbryo_GFPHistone_0013.tif │ ├── DrosophilaEmbryo_GFPHistone_0014.tif │ ├── DrosophilaEmbryo_GFPHistone_0015.tif │ ├── DrosophilaEmbryo_GFPHistone_0016.tif │ ├── DrosophilaEmbryo_GFPHistone_0017.tif │ ├── DrosophilaEmbryo_GFPHistone_0018.tif │ ├── DrosophilaEmbryo_GFPHistone_0019.tif │ └── DrosophilaEmbryo_GFPHistone_0020.tif ├── ExampleTumor ├── ExampleTumor.cppipe ├── README.md └── images │ ├── 30-2A1b.jpg │ └── 30-2A1f.jpg ├── ExampleUntangleWorms ├── ExampleUntangleWorms.cppipe ├── README.md └── images │ ├── 1649_1109_0003_Amp5-1_B_20070424_C01_w1_10E01AFB-34C4-416E-A9D3-51B90AB53728.tif │ ├── 1649_1109_0003_Amp5-1_B_20070424_C01_w2_CB2F18CD-EDF0-4BCD-98CF-3A07E5A582FF.tif │ ├── 1649_1109_0003_Amp5-1_B_20070424_C20_w1_0F5A41CB-2646-49E5-9281-F5B1F655B7BC.tif │ ├── 1649_1109_0003_Amp5-1_B_20070424_C20_w2_5F64C597-735E-435D-B49C-2A07B9D6DFC3.tif │ └── MyWormModel_B01_B24.xml ├── ExampleUntangleWormsBrightField ├── ExampleUntangleWormsBrightField.cppipe ├── README.md └── images │ ├── TrainingSetORO.xml │ ├── WT_orig.png │ └── fat_orig.png ├── ExampleVitraImages ├── ExampleVitra.cppipe ├── README.md └── images │ ├── Channel 1-01-A-01-00.tif │ ├── Channel 1-12-A-12-00.tif │ ├── Channel 2-01-A-01-00.tif │ ├── Channel 2-12-A-12-00.tif │ ├── VitraChannel1ILLUM.npy │ └── VitraChannel2ILLUM.npy ├── ExampleWoundHealing ├── ExampleWoundHealing.cppipe ├── README.md └── images │ ├── DMSO_B5_t0.JPG │ └── DMSO_B5_t24.JPG ├── ExampleYeastColonies ├── 85-Bray_CurrentProtocols_2015.pdf ├── ExampleYeastColonies.cppipe ├── README.md └── images │ ├── 6-1.jpg │ └── PlateTemplate.png ├── ExampleYeastPatches ├── ExampleYeastPatches.cppipe ├── README.md └── images │ └── 1832-48hours-gal-1.JPG ├── LICENSE ├── Makefile └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | 91 | .idea/ 92 | 93 | *.cpproj 94 | *.DS_Store 95 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | addons: 2 | apt: 3 | packages: 4 | - libhdf5-serial-dev 5 | - python-pip 6 | apt: true 7 | directories: $HOME/.cache/pip 8 | dist: trusty 9 | after_script: make clean 10 | env: 11 | - LC_ALL="en_US.UTF-8" CP_MYSQL_TEST_HOST="127.0.0.1" CP_MYSQL_TEST_USER="root" CP_MYSQL_TEST_PASSWORD="" 12 | install: 13 | - pip install --upgrade pip 14 | - pip install --upgrade cython 15 | - pip install --upgrade joblib 16 | - pip install --upgrade numpy 17 | - pip install --upgrade scipy 18 | - pip install --editable git+https://github.com/CellProfiler/CellProfiler.git#egg=CellProfiler 19 | - pip freeze 20 | language: python 21 | notifications: 22 | email: false 23 | slack: 24 | on_failure: always 25 | on_pull_requests: false 26 | on_success: never 27 | secure: 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 28 | python: 2.7 29 | script: 30 | - make test 31 | sudo: false 32 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleIlluminationCorrection_Example1_AllMethod.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:9 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Load the images by matching files in the folder against the unique text pattern \\xe2\\x80\\x98TIF\\xe2\\x80\\x99.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:1 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "AS_09047_") 43 | Name to assign these images:OrigGreen 44 | Name to assign these objects:Cell 45 | Select the image type:Color image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | 49 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 50 | Do you want to group your images?:No 51 | grouping metadata count:1 52 | Metadata category:None 53 | 54 | CorrectIlluminationCalculate:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Perform illumination correction using All images, the Regular method and a small median filter.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 55 | Select the input image:OrigGreen 56 | Name the output image:SuboptimalIllumGreen 57 | Select how the illumination function is calculated:Regular 58 | Dilate objects in the final averaged image?:No 59 | Dilation radius:1 60 | Block size:60 61 | Rescale the illumination function?:Yes 62 | Calculate function for each image individually, or based on all images?:All\x3A First cycle 63 | Smoothing method:Median Filter 64 | Method to calculate smoothing filter size:Manually 65 | Approximate object diameter:10 66 | Smoothing filter size:12 67 | Retain the averaged image?:No 68 | Name the averaged image:IllumBlueAvg 69 | Retain the dilated image?:No 70 | Name the dilated image:IllumBlueDilated 71 | Automatically calculate spline parameters?:Yes 72 | Background mode:auto 73 | Number of spline points:5 74 | Background threshold:2.0 75 | Image resampling factor:2.0 76 | Maximum number of iterations:40 77 | Residual value for convergence:0.001 78 | 79 | CorrectIlluminationApply:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Apply the illumination function to the original image and examine the result. In this case, the intensity variations reflect the cells more than the illumination, so this function is undesireable.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 80 | Select the input image:SuboptimalIllumGreen 81 | Name the output image:SuboptimalCorrGreen 82 | Select the illumination function:SuboptimalIllumGreen 83 | Select how the illumination function is applied:Divide 84 | 85 | CorrectIlluminationCalculate:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'This time, perform illumination correction using All images, the Regular method and a larger median filter.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 86 | Select the input image:OrigGreen 87 | Name the output image:OptimalIllumGreen 88 | Select how the illumination function is calculated:Regular 89 | Dilate objects in the final averaged image?:No 90 | Dilation radius:1 91 | Block size:60 92 | Rescale the illumination function?:Yes 93 | Calculate function for each image individually, or based on all images?:All\x3A First cycle 94 | Smoothing method:Median Filter 95 | Method to calculate smoothing filter size:Manually 96 | Approximate object diameter:10 97 | Smoothing filter size:125 98 | Retain the averaged image?:No 99 | Name the averaged image:IllumBlueAvg 100 | Retain the dilated image?:No 101 | Name the dilated image:IllumBlueDilated 102 | Automatically calculate spline parameters?:Yes 103 | Background mode:auto 104 | Number of spline points:5 105 | Background threshold:2.0 106 | Image resampling factor:2.0 107 | Maximum number of iterations:40 108 | Residual value for convergence:0.001 109 | 110 | CorrectIlluminationApply:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Apply the illumination function to the original image and examine the result. This time, the intensity variations in the illumination function have been smoothed out, producing a better result.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 111 | Select the input image:OrigGreen 112 | Name the output image:OptimalCorrGreen 113 | Select the illumination function:OptimalIllumGreen 114 | Select how the illumination function is applied:Divide 115 | 116 | SaveImages:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'You can save the final illumination function to a file for later use in an analysis pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 117 | Select the type of image to save:Image 118 | Select the image to save:OptimalIllumGreen 119 | Select method for constructing file names:Single name 120 | Select image name for file prefix:None 121 | Enter single file name:Illum 122 | Number of digits:4 123 | Append a suffix to the image file name?:No 124 | Text to append to the image name: 125 | Saved file format:npy 126 | Output file location:Default Output Folder\x7C 127 | Image bit depth:32-bit floating point 128 | Overwrite existing files without warning?:Yes 129 | When to save:First cycle 130 | Record the file and path information to the saved image?:No 131 | Create subfolders in the output folder?:No 132 | Base image folder:Elsewhere...\x7C 133 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleIlluminationCorrection_Example1_EachMethod.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:6 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Load one image from the full set.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:1 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "AS_09047_050428030001_O14f01d2.TIF") 43 | Name to assign these images:OrigGreen 44 | Name to assign these objects:Cell 45 | Select the image type:Grayscale image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | 49 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 50 | Do you want to group your images?:No 51 | grouping metadata count:1 52 | Metadata category:None 53 | 54 | CorrectIlluminationCalculate:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Perform illumination correction using the Regular method and a small median filter. The intensity variations reflect the cells more than the illumination, so this function is undesireable.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 55 | Select the input image:OrigGreen 56 | Name the output image:IllumGreen 57 | Select how the illumination function is calculated:Regular 58 | Dilate objects in the final averaged image?:No 59 | Dilation radius:1 60 | Block size:60 61 | Rescale the illumination function?:Yes 62 | Calculate function for each image individually, or based on all images?:Each 63 | Smoothing method:Median Filter 64 | Method to calculate smoothing filter size:Manually 65 | Approximate object diameter:10 66 | Smoothing filter size:25 67 | Retain the averaged image?:No 68 | Name the averaged image:IllumBlueAvg 69 | Retain the dilated image?:No 70 | Name the dilated image:IllumBlueDilated 71 | Automatically calculate spline parameters?:Yes 72 | Background mode:auto 73 | Number of spline points:5 74 | Background threshold:2.0 75 | Image resampling factor:2.0 76 | Maximum number of iterations:40 77 | Residual value for convergence:0.001 78 | 79 | CorrectIlluminationApply:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Apply the illumination function to the original image and examine the result.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 80 | Select the input image:OrigGreen 81 | Name the output image:CorrGreen 82 | Select the illumination function:IllumGreen 83 | Select how the illumination function is applied:Divide 84 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleIlluminationCorrection_Example2.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:11 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'The rule critieria specified will match one file only\x3A \\xe2\\x80\\x94W00001\\xe2\\x80\\x94P00001\\xe2\\x80\\x94Z00000\\xe2\\x80\\x94T00000\\xe2\\x80\\x94cherry.tif\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:1 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "--W00001") 43 | Name to assign these images:Grayscale 44 | Name to assign these objects:Cell 45 | Select the image type:Grayscale image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | 49 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 50 | Do you want to group your images?:No 51 | grouping metadata count:1 52 | Metadata category:None 53 | 54 | IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the well containing the worms.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 55 | Select the input image:Grayscale 56 | Name the primary objects to be identified:UncorrectedNuclei 57 | Typical diameter of objects, in pixel units (Min,Max):20,50 58 | Discard objects outside the diameter range?:No 59 | Discard objects touching the border of the image?:Yes 60 | Method to distinguish clumped objects:Intensity 61 | Method to draw dividing lines between clumped objects:Intensity 62 | Size of smoothing filter:10 63 | Suppress local maxima that are closer than this minimum allowed distance:7.0 64 | Speed up by using lower-resolution image to find local maxima?:Yes 65 | Fill holes in identified objects?:After both thresholding and declumping 66 | Automatically calculate size of smoothing filter for declumping?:Yes 67 | Automatically calculate minimum allowed distance between local maxima?:Yes 68 | Handling of objects if excessive number of objects identified:Continue 69 | Maximum number of objects:500 70 | Use advanced settings?:Yes 71 | Threshold setting version:10 72 | Threshold strategy:Global 73 | Thresholding method:Otsu 74 | Threshold smoothing scale:1.3488 75 | Threshold correction factor:1.0 76 | Lower and upper bounds on threshold:0.0,1.0 77 | Manual threshold:0.0 78 | Select the measurement to threshold with:None 79 | Two-class or three-class thresholding?:Three classes 80 | Assign pixels in the middle intensity class to the foreground or the background?:Background 81 | Size of adaptive window:50 82 | Lower outlier fraction:0.05 83 | Upper outlier fraction:0.05 84 | Averaging method:Mean 85 | Variance method:Standard deviation 86 | # of deviations:2.0 87 | Thresholding method:Otsu 88 | 89 | CorrectIlluminationCalculate:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'In this instance, we are doing background correction with a small block size and median filtering. Note that the worms are still visible in the illumination function.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 90 | Select the input image:Grayscale 91 | Name the output image:SmallBlockIllum 92 | Select how the illumination function is calculated:Background 93 | Dilate objects in the final averaged image?:No 94 | Dilation radius:1 95 | Block size:8 96 | Rescale the illumination function?:No 97 | Calculate function for each image individually, or based on all images?:Each 98 | Smoothing method:Median Filter 99 | Method to calculate smoothing filter size:Object size 100 | Approximate object diameter:16 101 | Smoothing filter size:10 102 | Retain the averaged image?:No 103 | Name the averaged image:IllumBlueAvg 104 | Retain the dilated image?:No 105 | Name the dilated image:IllumBlueDilated 106 | Automatically calculate spline parameters?:Yes 107 | Background mode:auto 108 | Number of spline points:5 109 | Background threshold:2.0 110 | Image resampling factor:2.0 111 | Maximum number of iterations:40 112 | Residual value for convergence:0.001 113 | 114 | CorrectIlluminationApply:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Now apply the illumination function to the original image and examine the result.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 115 | Select the input image:Grayscale 116 | Name the output image:SmallBlockCorrected 117 | Select the illumination function:SmallBlockIllum 118 | Select how the illumination function is applied:Subtract 119 | 120 | IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 121 | Select the input image:SmallBlockCorrected 122 | Name the primary objects to be identified:SmallBlockCorrectedNuclei 123 | Typical diameter of objects, in pixel units (Min,Max):20,50 124 | Discard objects outside the diameter range?:No 125 | Discard objects touching the border of the image?:Yes 126 | Method to distinguish clumped objects:Intensity 127 | Method to draw dividing lines between clumped objects:Intensity 128 | Size of smoothing filter:10 129 | Suppress local maxima that are closer than this minimum allowed distance:7.0 130 | Speed up by using lower-resolution image to find local maxima?:Yes 131 | Fill holes in identified objects?:After both thresholding and declumping 132 | Automatically calculate size of smoothing filter for declumping?:Yes 133 | Automatically calculate minimum allowed distance between local maxima?:Yes 134 | Handling of objects if excessive number of objects identified:Continue 135 | Maximum number of objects:500 136 | Use advanced settings?:Yes 137 | Threshold setting version:10 138 | Threshold strategy:Global 139 | Thresholding method:Otsu 140 | Threshold smoothing scale:1.3488 141 | Threshold correction factor:1.0 142 | Lower and upper bounds on threshold:0.0,1.0 143 | Manual threshold:0.0 144 | Select the measurement to threshold with:None 145 | Two-class or three-class thresholding?:Three classes 146 | Assign pixels in the middle intensity class to the foreground or the background?:Background 147 | Size of adaptive window:50 148 | Lower outlier fraction:0.05 149 | Upper outlier fraction:0.05 150 | Averaging method:Mean 151 | Variance method:Standard deviation 152 | # of deviations:2.0 153 | Thresholding method:Otsu 154 | 155 | CorrectIlluminationCalculate:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'In this instance, we are doing background correction with a larger block size and median filtering. This gets rid of the worm-like artifacts, but is still a bit blocky.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 156 | Select the input image:Grayscale 157 | Name the output image:LargeBlockIllum 158 | Select how the illumination function is calculated:Background 159 | Dilate objects in the final averaged image?:No 160 | Dilation radius:1 161 | Block size:8 162 | Rescale the illumination function?:No 163 | Calculate function for each image individually, or based on all images?:Each 164 | Smoothing method:Median Filter 165 | Method to calculate smoothing filter size:Manually 166 | Approximate object diameter:10 167 | Smoothing filter size:50 168 | Retain the averaged image?:No 169 | Name the averaged image:IllumBlueAvg 170 | Retain the dilated image?:No 171 | Name the dilated image:IllumBlueDilated 172 | Automatically calculate spline parameters?:Yes 173 | Background mode:auto 174 | Number of spline points:5 175 | Background threshold:2.0 176 | Image resampling factor:2.0 177 | Maximum number of iterations:40 178 | Residual value for convergence:0.001 179 | 180 | CorrectIlluminationApply:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Apply the illumination function to the original image and examine the result.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 181 | Select the input image:Grayscale 182 | Name the output image:LargeBlockCorrected 183 | Select the illumination function:LargeBlockIllum 184 | Select how the illumination function is applied:Subtract 185 | 186 | IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 187 | Select the input image:LargeBlockCorrected 188 | Name the primary objects to be identified:LargeBlockCorrectedNuclei 189 | Typical diameter of objects, in pixel units (Min,Max):20,50 190 | Discard objects outside the diameter range?:No 191 | Discard objects touching the border of the image?:Yes 192 | Method to distinguish clumped objects:Intensity 193 | Method to draw dividing lines between clumped objects:Intensity 194 | Size of smoothing filter:10 195 | Suppress local maxima that are closer than this minimum allowed distance:7.0 196 | Speed up by using lower-resolution image to find local maxima?:Yes 197 | Fill holes in identified objects?:After both thresholding and declumping 198 | Automatically calculate size of smoothing filter for declumping?:Yes 199 | Automatically calculate minimum allowed distance between local maxima?:Yes 200 | Handling of objects if excessive number of objects identified:Continue 201 | Maximum number of objects:500 202 | Use advanced settings?:Yes 203 | Threshold setting version:10 204 | Threshold strategy:Global 205 | Thresholding method:Otsu 206 | Threshold smoothing scale:1.3488 207 | Threshold correction factor:1.0 208 | Lower and upper bounds on threshold:0.0,1.0 209 | Manual threshold:0.0 210 | Select the measurement to threshold with:None 211 | Two-class or three-class thresholding?:Three classes 212 | Assign pixels in the middle intensity class to the foreground or the background?:Background 213 | Size of adaptive window:50 214 | Lower outlier fraction:0.05 215 | Upper outlier fraction:0.05 216 | Averaging method:Mean 217 | Variance method:Standard deviation 218 | # of deviations:2.0 219 | Thresholding method:Otsu 220 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleIlluminationCorrection_Example3.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:13 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'The rule criteria will select only one file from the full list\x3A ADSAStaphInfection2_A01_w2247376DD-6ADD-442D-AE47-F54A05F3EA94.tif\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:1 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "ADSAStaphInfection2_A01_w2") 43 | Name to assign these images:OrigWorms 44 | Name to assign these objects:Cell 45 | Select the image type:Grayscale image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | 49 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 50 | Do you want to group your images?:No 51 | grouping metadata count:1 52 | Metadata category:None 53 | 54 | IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the well containing the worms.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 55 | Select the input image:OrigWorms 56 | Name the primary objects to be identified:Well 57 | Typical diameter of objects, in pixel units (Min,Max):1,40 58 | Discard objects outside the diameter range?:No 59 | Discard objects touching the border of the image?:Yes 60 | Method to distinguish clumped objects:None 61 | Method to draw dividing lines between clumped objects:Intensity 62 | Size of smoothing filter:10 63 | Suppress local maxima that are closer than this minimum allowed distance:7.0 64 | Speed up by using lower-resolution image to find local maxima?:Yes 65 | Fill holes in identified objects?:After both thresholding and declumping 66 | Automatically calculate size of smoothing filter for declumping?:Yes 67 | Automatically calculate minimum allowed distance between local maxima?:Yes 68 | Handling of objects if excessive number of objects identified:Continue 69 | Maximum number of objects:500 70 | Use advanced settings?:Yes 71 | Threshold setting version:10 72 | Threshold strategy:Global 73 | Thresholding method:Otsu 74 | Threshold smoothing scale:1.3488 75 | Threshold correction factor:1.0 76 | Lower and upper bounds on threshold:0.0,1.0 77 | Manual threshold:0.0 78 | Select the measurement to threshold with:None 79 | Two-class or three-class thresholding?:Three classes 80 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 81 | Size of adaptive window:50 82 | Lower outlier fraction:0.05 83 | Upper outlier fraction:0.05 84 | Averaging method:Mean 85 | Variance method:Standard deviation 86 | # of deviations:2.0 87 | Thresholding method:Otsu 88 | 89 | ExpandOrShrinkObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Shrink the well object by a few pixels to get rid of the bright ring around the exterior. Without this step, the CorrectIlluminationCalc module will end up with a skewed result.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 90 | Select the input objects:Well 91 | Name the output objects:ShrunkenWell 92 | Select the operation:Shrink objects by a specified number of pixels 93 | Number of pixels by which to expand or shrink:5 94 | Fill holes in objects so that all objects shrink to a single point?:No 95 | 96 | ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert the intensity of the image, since the background method in CorrectIlluminationCalc assumes a light foreground and dark background.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 97 | Operation:Invert 98 | Raise the power of the result by:1.0 99 | Multiply the result by:1.0 100 | Add to result:0.0 101 | Set values less than 0 equal to 0?:Yes 102 | Set values greater than 1 equal to 1?:Yes 103 | Ignore the image masks?:No 104 | Name the output image:InvertedWorms 105 | Image or measurement?:Image 106 | Select the first image:OrigWorms 107 | Multiply the first image by:1.0 108 | Measurement: 109 | Image or measurement?:Image 110 | Select the second image: 111 | Multiply the second image by:1.0 112 | Measurement: 113 | 114 | MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Mask the inverted image external to the well. CorrectIlluminationCalculate will take the mask into account when computing the illumination function.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 115 | Select the input image:InvertedWorms 116 | Name the output image:MaskedInvertedWorms 117 | Use objects or an image as a mask?:Objects 118 | Select object for mask:ShrunkenWell 119 | Select image for mask:None 120 | Invert the mask?:No 121 | 122 | CorrectIlluminationCalculate:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'First, we attempt to perform background correction by fitting a polynomial to the background pixels of the image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 123 | Select the input image:MaskedInvertedWorms 124 | Name the output image:PolynomialIllum 125 | Select how the illumination function is calculated:Background 126 | Dilate objects in the final averaged image?:No 127 | Dilation radius:1 128 | Block size:2 129 | Rescale the illumination function?:No 130 | Calculate function for each image individually, or based on all images?:Each 131 | Smoothing method:Fit Polynomial 132 | Method to calculate smoothing filter size:Automatic 133 | Approximate object diameter:10 134 | Smoothing filter size:10 135 | Retain the averaged image?:No 136 | Name the averaged image:IllumBlueAvg 137 | Retain the dilated image?:No 138 | Name the dilated image:IllumBlueDilated 139 | Automatically calculate spline parameters?:Yes 140 | Background mode:auto 141 | Number of spline points:5 142 | Background threshold:2.0 143 | Image resampling factor:2.0 144 | Maximum number of iterations:40 145 | Residual value for convergence:0.001 146 | 147 | CorrectIlluminationApply:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'We then apply the illumination function to the original image by subtraction and examine the result. The background is effectively removed from the inverted image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 148 | Select the input image:MaskedInvertedWorms 149 | Name the output image:PolynomialCorrected 150 | Select the illumination function:PolynomialIllum 151 | Select how the illumination function is applied:Subtract 152 | 153 | MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'This time, we\\xe2\\x80\\x99ll use a different correctio method on the original image. Mask the original image external to the well. CorrectIlluminationCalculate will take the mask into account when computing the illumination function.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 154 | Select the input image:OrigWorms 155 | Name the output image:MaskedOrigWorms 156 | Use objects or an image as a mask?:Objects 157 | Select object for mask:ShrunkenWell 158 | Select image for mask:None 159 | Invert the mask?:No 160 | 161 | CorrectIlluminationCalculate:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Perform background correction using the convex hull method; see the help for \\xe2\\x80\\x98Smoothing method\\xe2\\x80\\x99 for more details on how this method works. \'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 162 | Select the input image:MaskedOrigWorms 163 | Name the output image:ConvexHullIllumWorm 164 | Select how the illumination function is calculated:Regular 165 | Dilate objects in the final averaged image?:No 166 | Dilation radius:1 167 | Block size:60 168 | Rescale the illumination function?:Yes 169 | Calculate function for each image individually, or based on all images?:Each 170 | Smoothing method:No smoothing 171 | Method to calculate smoothing filter size:Automatic 172 | Approximate object diameter:10 173 | Smoothing filter size:10 174 | Retain the averaged image?:No 175 | Name the averaged image:IllumBlueAvg 176 | Retain the dilated image?:No 177 | Name the dilated image:IllumBlueDilated 178 | Automatically calculate spline parameters?:Yes 179 | Background mode:auto 180 | Number of spline points:5 181 | Background threshold:2.0 182 | Image resampling factor:2.0 183 | Maximum number of iterations:40 184 | Residual value for convergence:0.001 185 | 186 | CorrectIlluminationApply:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Apply the illumination function to the original image by division and examine the result. The background is effectively removed from the original image. The corrected image would then need to be inverted using ImageMath for object identification.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 187 | Select the input image:MaskedOrigWorms 188 | Name the output image:ConvexHullCorrWorm 189 | Select the illumination function:ConvexHullIllumWorm 190 | Select how the illumination function is applied:Divide 191 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleSpeckles.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:12 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:2 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "hoe") 43 | Name to assign these images:OrigBlue 44 | Name to assign these objects:Cell 45 | Select the image type:Grayscale image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | Select the rule criteria:and (file does contain "h2ax") 49 | Name to assign these images:OrigGreen 50 | Name to assign these objects:Nucleus 51 | Select the image type:Grayscale image 52 | Set intensity range from:Image metadata 53 | Maximum intensity:255.0 54 | 55 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 56 | Do you want to group your images?:No 57 | grouping metadata count:1 58 | Metadata category:None 59 | 60 | IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the nuclei from the nuclear stain image. \'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 61 | Select the input image:OrigBlue 62 | Name the primary objects to be identified:Nuclei 63 | Typical diameter of objects, in pixel units (Min,Max):120,300 64 | Discard objects outside the diameter range?:Yes 65 | Discard objects touching the border of the image?:Yes 66 | Method to distinguish clumped objects:Shape 67 | Method to draw dividing lines between clumped objects:Shape 68 | Size of smoothing filter:10 69 | Suppress local maxima that are closer than this minimum allowed distance:7.0 70 | Speed up by using lower-resolution image to find local maxima?:Yes 71 | Fill holes in identified objects?:After both thresholding and declumping 72 | Automatically calculate size of smoothing filter for declumping?:Yes 73 | Automatically calculate minimum allowed distance between local maxima?:Yes 74 | Handling of objects if excessive number of objects identified:Continue 75 | Maximum number of objects:500 76 | Use advanced settings?:Yes 77 | Threshold setting version:10 78 | Threshold strategy:Global 79 | Thresholding method:Otsu 80 | Threshold smoothing scale:1.3488 81 | Threshold correction factor:1.0 82 | Lower and upper bounds on threshold:0.0,1.0 83 | Manual threshold:0.0 84 | Select the measurement to threshold with:None 85 | Two-class or three-class thresholding?:Two classes 86 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 87 | Size of adaptive window:50 88 | Lower outlier fraction:0.05 89 | Upper outlier fraction:0.05 90 | Averaging method:Mean 91 | Variance method:Standard deviation 92 | # of deviations:2.0 93 | Thresholding method:Otsu 94 | 95 | EnhanceOrSuppressFeatures:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:6|show_window:True|notes:\x5B\'Use filtering to enhance the foci speckles in the image. The feature size setting should be specified to be at least as large as the largest feature to be enhanced.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 96 | Select the input image:OrigGreen 97 | Name the output image:EnhancedGreen 98 | Select the operation:Enhance 99 | Feature size:10 100 | Feature type:Speckles 101 | Range of hole sizes:1,10 102 | Smoothing scale:2.0 103 | Shear angle:0.0 104 | Decay:0.95 105 | Enhancement method:Tubeness 106 | Speed and accuracy:Fast 107 | 108 | MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Mask the foci image using the nuclei objects.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 109 | Select the input image:EnhancedGreen 110 | Name the output image:MaskedGreen 111 | Use objects or an image as a mask?:Objects 112 | Select object for mask:Nuclei 113 | Select image for mask:None 114 | Invert the mask?:No 115 | 116 | IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the foci using per-object thresholding to compute a threshold for each individual nuclei object. Some manual adjustment of the smoothing filter size and maxima supression distance is required to optimize segmentation.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 117 | Select the input image:MaskedGreen 118 | Name the primary objects to be identified:h2ax 119 | Typical diameter of objects, in pixel units (Min,Max):4,35 120 | Discard objects outside the diameter range?:Yes 121 | Discard objects touching the border of the image?:Yes 122 | Method to distinguish clumped objects:Intensity 123 | Method to draw dividing lines between clumped objects:Intensity 124 | Size of smoothing filter:4 125 | Suppress local maxima that are closer than this minimum allowed distance:4 126 | Speed up by using lower-resolution image to find local maxima?:Yes 127 | Fill holes in identified objects?:After both thresholding and declumping 128 | Automatically calculate size of smoothing filter for declumping?:No 129 | Automatically calculate minimum allowed distance between local maxima?:No 130 | Handling of objects if excessive number of objects identified:Continue 131 | Maximum number of objects:500 132 | Use advanced settings?:Yes 133 | Threshold setting version:10 134 | Threshold strategy:Global 135 | Thresholding method:RobustBackground 136 | Threshold smoothing scale:1.3488 137 | Threshold correction factor:1.0 138 | Lower and upper bounds on threshold:0.0,1.0 139 | Manual threshold:0.0 140 | Select the measurement to threshold with:None 141 | Two-class or three-class thresholding?:Two classes 142 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 143 | Size of adaptive window:50 144 | Lower outlier fraction:0.05 145 | Upper outlier fraction:0.05 146 | Averaging method:Mean 147 | Variance method:Standard deviation 148 | # of deviations:2.0 149 | Thresholding method:Otsu 150 | 151 | MeasureObjectIntensity:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure the intensity of the nuclei against the nuclei image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 152 | Hidden:1 153 | Select an image to measure:OrigBlue 154 | Select objects to measure:Nuclei 155 | 156 | MeasureObjectIntensity:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure the intensity of the foci against the h2ax image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 157 | Hidden:1 158 | Select an image to measure:OrigGreen 159 | Select objects to measure:h2ax 160 | 161 | RelateObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Establish a parent-child between the foci (\\xe2\\x80\\x9cchildren\\xe2\\x80\\x9d) and the nuclei (\\xe2\\x80\\x9cparents\\xe2\\x80\\x9d) in order to determine which foci belong to which nuclei. Then, calculate mean foci measurements for each nucleus.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 162 | Parent objects:Nuclei 163 | Child objects:h2ax 164 | Calculate child-parent distances?:None 165 | Calculate per-parent means for all child measurements?:Yes 166 | Calculate distances to other parents?:No 167 | Parent name:None 168 | 169 | ExportToSpreadsheet:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:12|show_window:True|notes:\x5B\'Export any measurements to a comma-delimited file (.csv). The measurements made for the nuclei and foci objects will be saved to separate .csv files, in addition to the per-image .csv.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 170 | Select the column delimiter:Comma (",") 171 | Add image metadata columns to your object data file?:No 172 | Select the measurements to export:No 173 | Calculate the per-image mean values for object measurements?:No 174 | Calculate the per-image median values for object measurements?:No 175 | Calculate the per-image standard deviation values for object measurements?:No 176 | Output file location:Default Output Folder\x7C 177 | Create a GenePattern GCT file?:No 178 | Select source of sample row name:Metadata 179 | Select the image to use as the identifier:None 180 | Select the metadata to use as the identifier:None 181 | Export all measurement types?:No 182 | Press button to select measurements: 183 | Representation of Nan/Inf:NaN 184 | Add a prefix to file names?:No 185 | Filename prefix:MyExpt_ 186 | Overwrite existing files without warning?:Yes 187 | Data to export:Image 188 | Combine these object measurements with those of the previous object?:No 189 | File name:DATA.csv 190 | Use the object name for the file name?:Yes 191 | Data to export:Nuclei 192 | Combine these object measurements with those of the previous object?:No 193 | File name:DATA.csv 194 | Use the object name for the file name?:Yes 195 | Data to export:h2ax 196 | Combine these object measurements with those of the previous object?:No 197 | File name:DATA.csv 198 | Use the object name for the file name?:Yes 199 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleTumor.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:12 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:2 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain "f.jpg") 43 | Name to assign these images:ColorFluor 44 | Name to assign these objects:Cell 45 | Select the image type:Color image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | Select the rule criteria:and (file does contain "b.jpg") 49 | Name to assign these images:ColorLung 50 | Name to assign these objects:Cell 51 | Select the image type:Color image 52 | Set intensity range from:Image metadata 53 | Maximum intensity:255.0 54 | 55 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 56 | Do you want to group your images?:No 57 | grouping metadata count:1 58 | Metadata category:None 59 | 60 | ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 61 | Select the input image:ColorLung 62 | Conversion method:Combine 63 | Image type:RGB 64 | Name the output image:GrayLung 65 | Relative weight of the red channel:1.0 66 | Relative weight of the green channel:1.0 67 | Relative weight of the blue channel:1.0 68 | Convert red to gray?:Yes 69 | Name the output image:OrigRed 70 | Convert green to gray?:Yes 71 | Name the output image:OrigGreen 72 | Convert blue to gray?:Yes 73 | Name the output image:OrigBlue 74 | Convert hue to gray?:Yes 75 | Name the output image:OrigHue 76 | Convert saturation to gray?:Yes 77 | Name the output image:OrigSaturation 78 | Convert value to gray?:Yes 79 | Name the output image:OrigValue 80 | Channel count:1 81 | Channel number:Red\x3A 1 82 | Relative weight of the channel:1.0 83 | Image name:Channel1 84 | 85 | ColorToGray:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 86 | Select the input image:ColorFluor 87 | Conversion method:Split 88 | Image type:RGB 89 | Name the output image:OrigGray 90 | Relative weight of the red channel:1.0 91 | Relative weight of the green channel:1.0 92 | Relative weight of the blue channel:1.0 93 | Convert red to gray?:No 94 | Name the output image:OrigRed 95 | Convert green to gray?:Yes 96 | Name the output image:GrayTumor 97 | Convert blue to gray?:No 98 | Name the output image:OrigBlue 99 | Convert hue to gray?:Yes 100 | Name the output image:OrigHue 101 | Convert saturation to gray?:Yes 102 | Name the output image:OrigSaturation 103 | Convert value to gray?:Yes 104 | Name the output image:OrigValue 105 | Channel count:1 106 | Channel number:Red\x3A 1 107 | Relative weight of the channel:1.0 108 | Image name:Channel1 109 | 110 | IdentifyPrimaryObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 111 | Select the input image:GrayTumor 112 | Name the primary objects to be identified:tumor 113 | Typical diameter of objects, in pixel units (Min,Max):3,99999 114 | Discard objects outside the diameter range?:Yes 115 | Discard objects touching the border of the image?:Yes 116 | Method to distinguish clumped objects:Intensity 117 | Method to draw dividing lines between clumped objects:Intensity 118 | Size of smoothing filter:15 119 | Suppress local maxima that are closer than this minimum allowed distance:15 120 | Speed up by using lower-resolution image to find local maxima?:Yes 121 | Fill holes in identified objects?:After both thresholding and declumping 122 | Automatically calculate size of smoothing filter for declumping?:No 123 | Automatically calculate minimum allowed distance between local maxima?:No 124 | Handling of objects if excessive number of objects identified:Continue 125 | Maximum number of objects:500 126 | Use advanced settings?:Yes 127 | Threshold setting version:10 128 | Threshold strategy:Global 129 | Thresholding method:Minimum cross entropy 130 | Threshold smoothing scale:1.0 131 | Threshold correction factor:1.0 132 | Lower and upper bounds on threshold:0.0,1.0 133 | Manual threshold:0.0 134 | Select the measurement to threshold with:None 135 | Two-class or three-class thresholding?:Three classes 136 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 137 | Size of adaptive window:50 138 | Lower outlier fraction:0.05 139 | Upper outlier fraction:0.05 140 | Averaging method:Mean 141 | Variance method:Standard deviation 142 | # of deviations:2.0 143 | Thresholding method:Otsu 144 | 145 | MeasureObjectSizeShape:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 146 | Select objects to measure:tumor 147 | Calculate the Zernike features?:Yes 148 | 149 | ImageMath:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 150 | Operation:Add 151 | Raise the power of the result by:1.0 152 | Multiply the result by:1.0 153 | Add to result:0.0 154 | Set values less than 0 equal to 0?:Yes 155 | Set values greater than 1 equal to 1?:Yes 156 | Ignore the image masks?:No 157 | Name the output image:CombinedImage 158 | Image or measurement?:Image 159 | Select the first image:GrayLung 160 | Multiply the first image by:0.5 161 | Measurement: 162 | Image or measurement?:Image 163 | Select the second image:GrayTumor 164 | Multiply the second image by:0.5 165 | Measurement: 166 | 167 | OverlayOutlines:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 168 | Display outlines on a blank image?:No 169 | Select image on which to display outlines:CombinedImage 170 | Name the output image:TumorOutline 171 | Outline display mode:Color 172 | Select method to determine brightness of outlines:Max of image 173 | How to outline:Thick 174 | Select outline color:magenta 175 | Select objects to display:tumor 176 | 177 | SaveImages:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 178 | Select the type of image to save:Image 179 | Select the image to save:TumorOutline 180 | Select method for constructing file names:From image filename 181 | Select image name for file prefix:ColorFluor 182 | Enter single file name:OrigBlue 183 | Number of digits:4 184 | Append a suffix to the image file name?:Yes 185 | Text to append to the image name:_Tumors 186 | Saved file format:png 187 | Output file location:Default Output Folder\x7C 188 | Image bit depth:8-bit integer 189 | Overwrite existing files without warning?:Yes 190 | When to save:Every cycle 191 | Record the file and path information to the saved image?:No 192 | Create subfolders in the output folder?:No 193 | Base image folder:Elsewhere...\x7C 194 | 195 | ExportToSpreadsheet:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:12|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 196 | Select the column delimiter:Comma (",") 197 | Add image metadata columns to your object data file?:No 198 | Select the measurements to export:No 199 | Calculate the per-image mean values for object measurements?:No 200 | Calculate the per-image median values for object measurements?:No 201 | Calculate the per-image standard deviation values for object measurements?:No 202 | Output file location:Default Output Folder\x7C 203 | Create a GenePattern GCT file?:No 204 | Select source of sample row name:Metadata 205 | Select the image to use as the identifier:None 206 | Select the metadata to use as the identifier:None 207 | Export all measurement types?:Yes 208 | Press button to select measurements: 209 | Representation of Nan/Inf:NaN 210 | Add a prefix to file names?:No 211 | Filename prefix:MyExpt_ 212 | Overwrite existing files without warning?:Yes 213 | -------------------------------------------------------------------------------- /CellProfiler3Pipelines/ExampleWoundHealing.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:3 3 | DateRevision:300 4 | GitHash: 5 | ModuleCount:9 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) 21 | Regular expression to extract from folder name:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location: 25 | Match file and image metadata:\x5B\x5D 26 | Use case insensitive matching?:No 27 | 28 | NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Load the images by matching files in the folder against the unique text pattern \\xe2\\x80\\x98.JPG\\xe2\\x80\\x99\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 29 | Assign a name to:Images matching rules 30 | Select the image type:Grayscale image 31 | Name to assign these images:DNA 32 | Match metadata:\x5B\x5D 33 | Image set matching method:Order 34 | Set intensity range from:Image metadata 35 | Assignments count:1 36 | Single images count:0 37 | Maximum intensity:255.0 38 | Process as 3D?:No 39 | Relative pixel spacing in X:1.0 40 | Relative pixel spacing in Y:1.0 41 | Relative pixel spacing in Z:1.0 42 | Select the rule criteria:and (file does contain ".JPG") 43 | Name to assign these images:OrigColor 44 | Name to assign these objects:Cell 45 | Select the image type:Color image 46 | Set intensity range from:Image metadata 47 | Maximum intensity:255.0 48 | 49 | Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 50 | Do you want to group your images?:No 51 | grouping metadata count:1 52 | Metadata category:None 53 | 54 | ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Combine the color image into a grayscale image.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 55 | Select the input image:OrigColor 56 | Conversion method:Combine 57 | Image type:RGB 58 | Name the output image:OrigGray 59 | Relative weight of the red channel:1.0 60 | Relative weight of the green channel:1.0 61 | Relative weight of the blue channel:1.0 62 | Convert red to gray?:Yes 63 | Name the output image:OrigRed 64 | Convert green to gray?:Yes 65 | Name the output image:OrigGreen 66 | Convert blue to gray?:Yes 67 | Name the output image:OrigBlue 68 | Convert hue to gray?:Yes 69 | Name the output image:OrigHue 70 | Convert saturation to gray?:Yes 71 | Name the output image:OrigSaturation 72 | Convert value to gray?:Yes 73 | Name the output image:OrigValue 74 | Channel count:1 75 | Channel number:Red\x3A 1 76 | Relative weight of the channel:1.0 77 | Image name:Channel1 78 | 79 | Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Smooth the image using a Gaussian filter.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 80 | Select the input image:OrigGray 81 | Name the output image:Corrected 82 | Select smoothing method:Gaussian Filter 83 | Calculate artifact diameter automatically?:No 84 | Typical artifact diameter:20 85 | Edge intensity difference:0.1 86 | Clip intensities to 0 and 1?:Yes 87 | 88 | IdentifyPrimaryObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the tissue region using three-class Otsu.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 89 | Select the input image:Corrected 90 | Name the primary objects to be identified:Tissue 91 | Typical diameter of objects, in pixel units (Min,Max):10,40 92 | Discard objects outside the diameter range?:No 93 | Discard objects touching the border of the image?:No 94 | Method to distinguish clumped objects:None 95 | Method to draw dividing lines between clumped objects:Intensity 96 | Size of smoothing filter:10 97 | Suppress local maxima that are closer than this minimum allowed distance:7.0 98 | Speed up by using lower-resolution image to find local maxima?:Yes 99 | Fill holes in identified objects?:Never 100 | Automatically calculate size of smoothing filter for declumping?:Yes 101 | Automatically calculate minimum allowed distance between local maxima?:Yes 102 | Handling of objects if excessive number of objects identified:Continue 103 | Maximum number of objects:500 104 | Use advanced settings?:Yes 105 | Threshold setting version:10 106 | Threshold strategy:Global 107 | Thresholding method:Otsu 108 | Threshold smoothing scale:1.3488 109 | Threshold correction factor:1.0 110 | Lower and upper bounds on threshold:0.0,1.0 111 | Manual threshold:0.0 112 | Select the measurement to threshold with:None 113 | Two-class or three-class thresholding?:Three classes 114 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 115 | Size of adaptive window:50 116 | Lower outlier fraction:0.05 117 | Upper outlier fraction:0.05 118 | Averaging method:Mean 119 | Variance method:Standard deviation 120 | # of deviations:2.0 121 | Thresholding method:Otsu 122 | 123 | MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Measure the area occupied by the tissue region.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 124 | Hidden:1 125 | Measure the area occupied in a binary image, or in objects?:Objects 126 | Select objects to measure:Tissue 127 | Select a binary image to measure:None 128 | 129 | ExportToSpreadsheet:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:12|show_window:True|notes:\x5B\'Export any measurements to a comma-delimited file (.csv). Since the tissue area is an image measurement, it is included in the per-image file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] 130 | Select the column delimiter:Comma (",") 131 | Add image metadata columns to your object data file?:No 132 | Select the measurements to export:No 133 | Calculate the per-image mean values for object measurements?:Yes 134 | Calculate the per-image median values for object measurements?:No 135 | Calculate the per-image standard deviation values for object measurements?:No 136 | Output file location:Default Output Folder\x7C 137 | Create a GenePattern GCT file?:No 138 | Select source of sample row name:Metadata 139 | Select the image to use as the identifier:None 140 | Select the metadata to use as the identifier:None 141 | Export all measurement types?:No 142 | Press button to select measurements: 143 | Representation of Nan/Inf:NaN 144 | Add a prefix to file names?:No 145 | Filename prefix:MyExpt_ 146 | Overwrite existing files without warning?:Yes 147 | Data to export:Image 148 | Combine these object measurements with those of the previous object?:No 149 | File name:DATA.csv 150 | Use the object name for the file name?:Yes 151 | -------------------------------------------------------------------------------- /ExampleColocalization/README.md: -------------------------------------------------------------------------------- 1 | Measuring the colocalization between fluorescently labeled molecules is a widely used approach to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identifcation and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels. 2 | 3 | About these images: 4 | 5 | Fluoresecent images of a histone-modified nucleosome that is labeled with a Cy3-like dye and an antibody labeled with a Cy5-like dye that is sensitive to to the histone modifications. 6 | 7 | These images were contributed by Jeff Reifenberger of Brad Berstein's Lab at Massachusetts General Hospital, March 2012 8 | -------------------------------------------------------------------------------- /ExampleColocalization/images/0_1_N_G.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleColocalization/images/0_1_N_G.png -------------------------------------------------------------------------------- /ExampleColocalization/images/0_1_N_R.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleColocalization/images/0_1_N_R.png -------------------------------------------------------------------------------- /ExampleColocalization/images/0_2_N_G.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleColocalization/images/0_2_N_G.png -------------------------------------------------------------------------------- /ExampleColocalization/images/0_2_N_R.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleColocalization/images/0_2_N_R.png -------------------------------------------------------------------------------- /ExampleCometAssay/README.md: -------------------------------------------------------------------------------- 1 | The fluorescent comet images were donated by Scott Floyd and Michael Pacold. The silver-stained comets were contributed by Jorge Ernesto González from the Centro de Protección e Higiene de Las Radiaciones (CPHR). 2 | -------------------------------------------------------------------------------- /ExampleCometAssay/images/CometTails.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleCometAssay/images/CometTails.tif -------------------------------------------------------------------------------- /ExampleCometAssay/images/NoTails.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleCometAssay/images/NoTails.tif -------------------------------------------------------------------------------- /ExampleFly/ExampleFly.csv: -------------------------------------------------------------------------------- 1 | Image_FileName_OrigBlue,Image_FileName_OrigGreen,Image_FileName_OrigRed 2 | http://cellprofiler.org/ExampleFlyImages/01_POS002_D.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS002_F.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS002_R.TIF 3 | http://cellprofiler.org/ExampleFlyImages/01_POS076_D.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS076_F.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS076_R.TIF 4 | http://cellprofiler.org/ExampleFlyImages/01_POS218_D.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS218_F.TIF,http://cellprofiler.org/ExampleFlyImages/01_POS218_R.TIF 5 | -------------------------------------------------------------------------------- /ExampleFly/images/01_POS002_D.TIF: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleFly/images/01_POS218_D.TIF -------------------------------------------------------------------------------- /ExampleFly/images/01_POS218_F.TIF: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleFly/images/01_POS218_F.TIF -------------------------------------------------------------------------------- /ExampleFly/images/01_POS218_R.TIF: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleFly/images/01_POS218_R.TIF -------------------------------------------------------------------------------- /ExampleHuman/README.md: -------------------------------------------------------------------------------- 1 | The images of human cells were provided by Jason Moffat. 2 | 3 | Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B, Grenier JK, Carpenter AE, Foo SY, Stewart SA, Stockwell BR, Hacohen N, Hahn WC, Lander ES, Sabatini DM, Root DE (2006) A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell, 124(6):1283-98 / doi: 10.1016/j.cell.2006.01.040. PMID 16564017 4 | 5 | The images in this example are licensed as CC-0. 6 | -------------------------------------------------------------------------------- /ExampleHuman/images/AS_09125_050116030001_D03f00d0.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleHuman/images/AS_09125_050116030001_D03f00d0.tif -------------------------------------------------------------------------------- /ExampleHuman/images/AS_09125_050116030001_D03f00d1.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleHuman/images/AS_09125_050116030001_D03f00d1.tif -------------------------------------------------------------------------------- /ExampleHuman/images/AS_09125_050116030001_D03f00d2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleHuman/images/AS_09125_050116030001_D03f00d2.tif -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example1_AllMethod.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:5 3 | DateRevision:400 4 | GitHash: 5 | ModuleCount:9 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', 'âx80x94', 'Load the images by matching files in the folder against the unique text pattern âx80x98TIFâx80x99.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:1 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain "AS_09047_") 45 | Name to assign these images:OrigGreen 46 | Name to assign these objects:Cell 47 | Select the image type:Color image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | 51 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 52 | Do you want to group your images?:No 53 | grouping metadata count:1 54 | Metadata category:None 55 | 56 | CorrectIlluminationCalculate:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Perform illumination correction using All images, the Regular method and a small median filter.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 57 | Select the input image:OrigGreen 58 | Name the output image:SuboptimalIllumGreen 59 | Select how the illumination function is calculated:Regular 60 | Dilate objects in the final averaged image?:No 61 | Dilation radius:1 62 | Block size:60 63 | Rescale the illumination function?:Yes 64 | Calculate function for each image individually, or based on all images?:All: First cycle 65 | Smoothing method:Median Filter 66 | Method to calculate smoothing filter size:Manually 67 | Approximate object diameter:10 68 | Smoothing filter size:12 69 | Retain the averaged image?:No 70 | Name the averaged image:IllumBlueAvg 71 | Retain the dilated image?:No 72 | Name the dilated image:IllumBlueDilated 73 | Automatically calculate spline parameters?:Yes 74 | Background mode:auto 75 | Number of spline points:5 76 | Background threshold:2.0 77 | Image resampling factor:2.0 78 | Maximum number of iterations:40 79 | Residual value for convergence:0.001 80 | 81 | CorrectIlluminationApply:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Apply the illumination function to the original image and examine the result. In this case, the intensity variations reflect the cells more than the illumination, so this function is undesireable.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 82 | Select the input image:OrigGreen 83 | Name the output image:SuboptimalCorrGreen 84 | Select the illumination function:SuboptimalIllumGreen 85 | Select how the illumination function is applied:Divide 86 | Set output image values less than 0 equal to 0?:Yes 87 | Set output image values greater than 1 equal to 1?:Yes 88 | 89 | CorrectIlluminationCalculate:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['This time, perform illumination correction using All images, the Regular method and a larger median filter.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 90 | Select the input image:OrigGreen 91 | Name the output image:OptimalIllumGreen 92 | Select how the illumination function is calculated:Regular 93 | Dilate objects in the final averaged image?:No 94 | Dilation radius:1 95 | Block size:60 96 | Rescale the illumination function?:Yes 97 | Calculate function for each image individually, or based on all images?:All: First cycle 98 | Smoothing method:Median Filter 99 | Method to calculate smoothing filter size:Manually 100 | Approximate object diameter:10 101 | Smoothing filter size:125 102 | Retain the averaged image?:No 103 | Name the averaged image:IllumBlueAvg 104 | Retain the dilated image?:No 105 | Name the dilated image:IllumBlueDilated 106 | Automatically calculate spline parameters?:Yes 107 | Background mode:auto 108 | Number of spline points:5 109 | Background threshold:2.0 110 | Image resampling factor:2.0 111 | Maximum number of iterations:40 112 | Residual value for convergence:0.001 113 | 114 | CorrectIlluminationApply:[module_num:8|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Apply the illumination function to the original image and examine the result. This time, the intensity variations in the illumination function have been smoothed out, producing a better result.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 115 | Select the input image:OrigGreen 116 | Name the output image:OptimalCorrGreen 117 | Select the illumination function:OptimalIllumGreen 118 | Select how the illumination function is applied:Divide 119 | Set output image values less than 0 equal to 0?:Yes 120 | Set output image values greater than 1 equal to 1?:Yes 121 | 122 | SaveImages:[module_num:9|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['You can save the final illumination function to a file for later use in an analysis pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 123 | Select the type of image to save:Image 124 | Select the image to save:OptimalIllumGreen 125 | Select method for constructing file names:Single name 126 | Select image name for file prefix:None 127 | Enter single file name:Illum 128 | Number of digits:4 129 | Append a suffix to the image file name?:No 130 | Text to append to the image name: 131 | Saved file format:npy 132 | Output file location:Default Output Folder| 133 | Image bit depth:32-bit floating point 134 | Overwrite existing files without warning?:Yes 135 | When to save:First cycle 136 | Record the file and path information to the saved image?:No 137 | Create subfolders in the output folder?:No 138 | Base image folder:Elsewhere...| 139 | How to save the series:T (Time) 140 | -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example1_EachMethod.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:5 3 | DateRevision:400 4 | GitHash: 5 | ModuleCount:6 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', 'âx80x94', 'Load one image from the full set.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:1 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain "AS_09047_050428030001_O14f01d2.TIF") 45 | Name to assign these images:OrigGreen 46 | Name to assign these objects:Cell 47 | Select the image type:Grayscale image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | 51 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 52 | Do you want to group your images?:No 53 | grouping metadata count:1 54 | Metadata category:None 55 | 56 | CorrectIlluminationCalculate:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Perform illumination correction using the Regular method and a small median filter. The intensity variations reflect the cells more than the illumination, so this function is undesireable.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 57 | Select the input image:OrigGreen 58 | Name the output image:IllumGreen 59 | Select how the illumination function is calculated:Regular 60 | Dilate objects in the final averaged image?:No 61 | Dilation radius:1 62 | Block size:60 63 | Rescale the illumination function?:Yes 64 | Calculate function for each image individually, or based on all images?:Each 65 | Smoothing method:Median Filter 66 | Method to calculate smoothing filter size:Manually 67 | Approximate object diameter:10 68 | Smoothing filter size:40 69 | Retain the averaged image?:No 70 | Name the averaged image:IllumBlueAvg 71 | Retain the dilated image?:No 72 | Name the dilated image:IllumBlueDilated 73 | Automatically calculate spline parameters?:Yes 74 | Background mode:auto 75 | Number of spline points:5 76 | Background threshold:2.0 77 | Image resampling factor:2.0 78 | Maximum number of iterations:40 79 | Residual value for convergence:0.001 80 | 81 | CorrectIlluminationApply:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Apply the illumination function to the original image and examine the result.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 82 | Select the input image:OrigGreen 83 | Name the output image:CorrGreen 84 | Select the illumination function:IllumGreen 85 | Select how the illumination function is applied:Divide 86 | Set output image values less than 0 equal to 0?:Yes 87 | Set output image values greater than 1 equal to 1?:Yes 88 | -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example3.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:5 3 | DateRevision:400 4 | GitHash: 5 | ModuleCount:13 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', 'â\x80\x94', 'The rule criteria will select only one file from the full list: ADSAStaphInfection2_A01_w2247376DD-6ADD-442D-AE47-F54A05F3EA94.tif']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:1 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain "ADSAStaphInfection2_A01_w2") 45 | Name to assign these images:OrigWorms 46 | Name to assign these objects:Cell 47 | Select the image type:Grayscale image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | 51 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 52 | Do you want to group your images?:No 53 | grouping metadata count:1 54 | Metadata category:None 55 | 56 | IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify the well containing the worms.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 57 | Select the input image:OrigWorms 58 | Name the primary objects to be identified:Well 59 | Typical diameter of objects, in pixel units (Min,Max):1,40 60 | Discard objects outside the diameter range?:No 61 | Discard objects touching the border of the image?:Yes 62 | Method to distinguish clumped objects:None 63 | Method to draw dividing lines between clumped objects:Intensity 64 | Size of smoothing filter:10 65 | Suppress local maxima that are closer than this minimum allowed distance:7.0 66 | Speed up by using lower-resolution image to find local maxima?:Yes 67 | Fill holes in identified objects?:After both thresholding and declumping 68 | Automatically calculate size of smoothing filter for declumping?:Yes 69 | Automatically calculate minimum allowed distance between local maxima?:Yes 70 | Handling of objects if excessive number of objects identified:Continue 71 | Maximum number of objects:500 72 | Display accepted local maxima?:No 73 | Select maxima color:Blue 74 | Use advanced settings?:Yes 75 | Threshold setting version:11 76 | Threshold strategy:Global 77 | Thresholding method:Otsu 78 | Threshold smoothing scale:1.3488 79 | Threshold correction factor:1.0 80 | Lower and upper bounds on threshold:0.0,1.0 81 | Manual threshold:0.0 82 | Select the measurement to threshold with:None 83 | Two-class or three-class thresholding?:Three classes 84 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 85 | Size of adaptive window:50 86 | Lower outlier fraction:0.05 87 | Upper outlier fraction:0.05 88 | Averaging method:Mean 89 | Variance method:Standard deviation 90 | # of deviations:2.0 91 | Thresholding method:Otsu 92 | 93 | ExpandOrShrinkObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Shrink the well object by a few pixels to get rid of the bright ring around the exterior. Without this step, the CorrectIlluminationCalc module will end up with a skewed result.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 94 | Select the input objects:Well 95 | Name the output objects:ShrunkenWell 96 | Select the operation:Shrink objects by a specified number of pixels 97 | Number of pixels by which to expand or shrink:5 98 | Fill holes in objects so that all objects shrink to a single point?:No 99 | 100 | ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Invert the intensity of the image, since the background method in CorrectIlluminationCalc assumes a light foreground and dark background.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 101 | Operation:Invert 102 | Raise the power of the result by:1.0 103 | Multiply the result by:1.0 104 | Add to result:0.0 105 | Set values less than 0 equal to 0?:Yes 106 | Set values greater than 1 equal to 1?:Yes 107 | Replace invalid values with 0?:Yes 108 | Ignore the image masks?:No 109 | Name the output image:InvertedWorms 110 | Image or measurement?:Image 111 | Select the first image:OrigWorms 112 | Multiply the first image by:1.0 113 | Measurement: 114 | Image or measurement?:Image 115 | Select the second image: 116 | Multiply the second image by:1.0 117 | Measurement: 118 | 119 | MaskImage:[module_num:8|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['Mask the inverted image external to the well. CorrectIlluminationCalculate will take the mask into account when computing the illumination function.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 120 | Select the input image:InvertedWorms 121 | Name the output image:MaskedInvertedWorms 122 | Use objects or an image as a mask?:Objects 123 | Select object for mask:ShrunkenWell 124 | Select image for mask:None 125 | Invert the mask?:No 126 | 127 | CorrectIlluminationCalculate:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['First, we attempt to perform background correction by fitting a polynomial to the background pixels of the image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 128 | Select the input image:MaskedInvertedWorms 129 | Name the output image:PolynomialIllum 130 | Select how the illumination function is calculated:Background 131 | Dilate objects in the final averaged image?:No 132 | Dilation radius:1 133 | Block size:2 134 | Rescale the illumination function?:No 135 | Calculate function for each image individually, or based on all images?:Each 136 | Smoothing method:Fit Polynomial 137 | Method to calculate smoothing filter size:Automatic 138 | Approximate object diameter:10 139 | Smoothing filter size:10 140 | Retain the averaged image?:No 141 | Name the averaged image:IllumBlueAvg 142 | Retain the dilated image?:No 143 | Name the dilated image:IllumBlueDilated 144 | Automatically calculate spline parameters?:Yes 145 | Background mode:auto 146 | Number of spline points:5 147 | Background threshold:2.0 148 | Image resampling factor:2.0 149 | Maximum number of iterations:40 150 | Residual value for convergence:0.001 151 | 152 | CorrectIlluminationApply:[module_num:10|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['We then apply the illumination function to the original image by subtraction and examine the result. The background is effectively removed from the inverted image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 153 | Select the input image:MaskedInvertedWorms 154 | Name the output image:PolynomialCorrected 155 | Select the illumination function:PolynomialIllum 156 | Select how the illumination function is applied:Subtract 157 | Set output image values less than 0 equal to 0?:Yes 158 | Set output image values greater than 1 equal to 1?:Yes 159 | 160 | MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['This time, weâ\x80\x99ll use a different correctio method on the original image. Mask the original image external to the well. CorrectIlluminationCalculate will take the mask into account when computing the illumination function.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 161 | Select the input image:OrigWorms 162 | Name the output image:MaskedOrigWorms 163 | Use objects or an image as a mask?:Objects 164 | Select object for mask:ShrunkenWell 165 | Select image for mask:None 166 | Invert the mask?:No 167 | 168 | CorrectIlluminationCalculate:[module_num:12|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Perform background correction using the convex hull method; see the help for â\x80\x98Smoothing methodâ\x80\x99 for more details on how this method works. ']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 169 | Select the input image:MaskedOrigWorms 170 | Name the output image:ConvexHullIllumWorm 171 | Select how the illumination function is calculated:Regular 172 | Dilate objects in the final averaged image?:No 173 | Dilation radius:1 174 | Block size:60 175 | Rescale the illumination function?:Yes 176 | Calculate function for each image individually, or based on all images?:Each 177 | Smoothing method:Convex Hull 178 | Method to calculate smoothing filter size:Automatic 179 | Approximate object diameter:10 180 | Smoothing filter size:10 181 | Retain the averaged image?:No 182 | Name the averaged image:IllumBlueAvg 183 | Retain the dilated image?:No 184 | Name the dilated image:IllumBlueDilated 185 | Automatically calculate spline parameters?:Yes 186 | Background mode:auto 187 | Number of spline points:5 188 | Background threshold:2.0 189 | Image resampling factor:2.0 190 | Maximum number of iterations:40 191 | Residual value for convergence:0.001 192 | 193 | CorrectIlluminationApply:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Apply the illumination function to the original image by division and examine the result. The background is effectively removed from the original image. The corrected image would then need to be inverted using ImageMath for object identification.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 194 | Select the input image:MaskedOrigWorms 195 | Name the output image:ConvexHullCorrWorm 196 | Select the illumination function:ConvexHullIllumWorm 197 | Select how the illumination function is applied:Divide 198 | Set output image values less than 0 equal to 0?:Yes 199 | Set output image values greater than 1 equal to 1?:Yes 200 | -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/ExampleIlluminationCorrection_Tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleIlluminationCorrection/ExampleIlluminationCorrection_Tutorial.pdf -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/README.md: -------------------------------------------------------------------------------- 1 | Illumination correction is often important for both accurate segmentation and for intensity measurements. This example shows how the CorrectIlluminationCalculate and CorrectIlluminationApply modules are used to compensate for the non-uniformities in illumination often present in microscopy images. 2 | 3 | The pipelines prefixed with "ExampleIlluminationCorrection_Example1" correspond to the images with the prefixed "AS_09047_". 4 | These images comprise the GFP channel from the SBS Roche Transfluor image set, available from http://www.broadinstitute.org/bbbc/sbs_roche_transfluor.html. 5 | 6 | The pipelines prefixed with "ExampleIlluminationCorrection_Example2" correspond to the images with the prefixed "—W00001—". 7 | 8 | The pipelines prefixed with "ExampleIlluminationCorrection_Example3" correspond to the images with the prefixed "ADSAStaphInfection2_". 9 | -------------------------------------------------------------------------------- /ExampleIlluminationCorrection/images/--W00001--P00001--Z00000--T00000--cherry.tif: 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This is typically seen in downstream analysis of Imaging flow cytometry, where hundred thousands of images of single cells are collected and stitched together in a montage. 2 | 3 | In this pipeline, we identify objects based on the brightfield signals, in an attempt to quantify cellular features in label-free experiments. 4 | 5 | See more Imaging flow cytometry data analysis at: http://cellprofiler.org/imagingflowcytometry/ 6 | 7 | Open-source stitching script (python) can be downloaded at: https://github.com/CellProfiler/stitching 8 | -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch1_1.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch1_1.tif -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch1_2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch1_2.tif -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch6_1.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch6_1.tif -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch6_2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch6_2.tif -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch7_1.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch7_1.tif -------------------------------------------------------------------------------- /ExampleImagingFlowCytometryObjectsInGrid/images/Ch7_2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleImagingFlowCytometryObjectsInGrid/images/Ch7_2.tif -------------------------------------------------------------------------------- /ExampleNeighbors/README.md: -------------------------------------------------------------------------------- 1 | Tissue samples often have irregularly shaped cells with adjacent edges. This pipeline shows how to input a color tissue image, split it into its component channels, and then identify individual cells from a particular stain and record the number of neighbors that each cell has. 2 | -------------------------------------------------------------------------------- /ExampleNeighbors/images/Clones1.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleNeighbors/images/Clones1.JPG -------------------------------------------------------------------------------- /ExamplePercentPositive/README.md: -------------------------------------------------------------------------------- 1 | Image from Jason Mitotic index experiment. 2 | -------------------------------------------------------------------------------- /ExamplePercentPositive/images/AS_09125_050116030001_D03f00d0.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExamplePercentPositive/images/AS_09125_050116030001_D03f00d0.tif -------------------------------------------------------------------------------- /ExamplePercentPositive/images/AS_09125_050116030001_D03f00d1.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExamplePercentPositive/images/AS_09125_050116030001_D03f00d1.tif -------------------------------------------------------------------------------- /ExampleSpeckles/ExampleSpeckles.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:5 3 | DateRevision:400 4 | GitHash: 5 | ModuleCount:12 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:2 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain "hoe") 45 | Name to assign these images:OrigBlue 46 | Name to assign these objects:Cell 47 | Select the image type:Grayscale image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | Select the rule criteria:and (file does contain "h2ax") 51 | Name to assign these images:OrigGreen 52 | Name to assign these objects:Nucleus 53 | Select the image type:Grayscale image 54 | Set intensity range from:Image metadata 55 | Maximum intensity:255.0 56 | 57 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 58 | Do you want to group your images?:No 59 | grouping metadata count:1 60 | Metadata category:None 61 | 62 | IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify the nuclei from the nuclear stain image. ']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 63 | Select the input image:OrigBlue 64 | Name the primary objects to be identified:Nuclei 65 | Typical diameter of objects, in pixel units (Min,Max):120,300 66 | Discard objects outside the diameter range?:Yes 67 | Discard objects touching the border of the image?:Yes 68 | Method to distinguish clumped objects:Shape 69 | Method to draw dividing lines between clumped objects:Shape 70 | Size of smoothing filter:10 71 | Suppress local maxima that are closer than this minimum allowed distance:7.0 72 | Speed up by using lower-resolution image to find local maxima?:Yes 73 | Fill holes in identified objects?:After both thresholding and declumping 74 | Automatically calculate size of smoothing filter for declumping?:Yes 75 | Automatically calculate minimum allowed distance between local maxima?:Yes 76 | Handling of objects if excessive number of objects identified:Continue 77 | Maximum number of objects:500 78 | Display accepted local maxima?:No 79 | Select maxima color:Blue 80 | Use advanced settings?:Yes 81 | Threshold setting version:11 82 | Threshold strategy:Global 83 | Thresholding method:Otsu 84 | Threshold smoothing scale:1.3488 85 | Threshold correction factor:1.0 86 | Lower and upper bounds on threshold:0.0,1.0 87 | Manual threshold:0.0 88 | Select the measurement to threshold with:None 89 | Two-class or three-class thresholding?:Two classes 90 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 91 | Size of adaptive window:50 92 | Lower outlier fraction:0.05 93 | Upper outlier fraction:0.05 94 | Averaging method:Mean 95 | Variance method:Standard deviation 96 | # of deviations:2.0 97 | Thresholding method:Otsu 98 | 99 | EnhanceOrSuppressFeatures:[module_num:6|svn_version:'Unknown'|variable_revision_number:7|show_window:True|notes:['Use filtering to enhance the foci speckles in the image. The feature size setting should be specified to be at least as large as the largest feature to be enhanced.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 100 | Select the input image:OrigGreen 101 | Name the output image:EnhancedGreen 102 | Select the operation:Enhance 103 | Feature size:10 104 | Feature type:Speckles 105 | Range of hole sizes:1,10 106 | Smoothing scale:2.0 107 | Shear angle:0.0 108 | Decay:0.95 109 | Enhancement method:Tubeness 110 | Speed and accuracy:Fast 111 | Rescale result image:Yes 112 | 113 | MaskImage:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['Mask the foci image using the nuclei objects.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 114 | Select the input image:EnhancedGreen 115 | Name the output image:MaskedGreen 116 | Use objects or an image as a mask?:Objects 117 | Select object for mask:Nuclei 118 | Select image for mask:None 119 | Invert the mask?:No 120 | 121 | IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify the foci using per-object thresholding to compute a threshold for each individual nuclei object. Some manual adjustment of the smoothing filter size and maxima supression distance is required to optimize segmentation.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 122 | Select the input image:MaskedGreen 123 | Name the primary objects to be identified:h2ax 124 | Typical diameter of objects, in pixel units (Min,Max):4,35 125 | Discard objects outside the diameter range?:Yes 126 | Discard objects touching the border of the image?:Yes 127 | Method to distinguish clumped objects:Intensity 128 | Method to draw dividing lines between clumped objects:Intensity 129 | Size of smoothing filter:4 130 | Suppress local maxima that are closer than this minimum allowed distance:4 131 | Speed up by using lower-resolution image to find local maxima?:Yes 132 | Fill holes in identified objects?:After both thresholding and declumping 133 | Automatically calculate size of smoothing filter for declumping?:No 134 | Automatically calculate minimum allowed distance between local maxima?:No 135 | Handling of objects if excessive number of objects identified:Continue 136 | Maximum number of objects:500 137 | Display accepted local maxima?:No 138 | Select maxima color:Blue 139 | Use advanced settings?:Yes 140 | Threshold setting version:11 141 | Threshold strategy:Global 142 | Thresholding method:Robust Background 143 | Threshold smoothing scale:1.3488 144 | Threshold correction factor:1.0 145 | Lower and upper bounds on threshold:0.0,1.0 146 | Manual threshold:0.0 147 | Select the measurement to threshold with:None 148 | Two-class or three-class thresholding?:Two classes 149 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 150 | Size of adaptive window:50 151 | Lower outlier fraction:0.05 152 | Upper outlier fraction:0.05 153 | Averaging method:Mean 154 | Variance method:Standard deviation 155 | # of deviations:2.0 156 | Thresholding method:Otsu 157 | 158 | MeasureObjectIntensity:[module_num:9|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure the intensity of the nuclei against the nuclei image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 159 | Select images to measure:OrigBlue 160 | Select objects to measure:Nuclei 161 | 162 | MeasureObjectIntensity:[module_num:10|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure the intensity of the foci against the h2ax image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 163 | Select images to measure:OrigGreen 164 | Select objects to measure:h2ax 165 | 166 | RelateObjects:[module_num:11|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Establish a parent-child between the foci (â\x80\x9cchildrenâ\x80\x9d) and the nuclei (â\x80\x9cparentsâ\x80\x9d) in order to determine which foci belong to which nuclei. Then, calculate mean foci measurements for each nucleus.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 167 | Parent objects:Nuclei 168 | Child objects:h2ax 169 | Calculate child-parent distances?:None 170 | Calculate per-parent means for all child measurements?:Yes 171 | Calculate distances to other parents?:No 172 | Do you want to save the children with parents as a new object set?:No 173 | Name the output object:None 174 | Parent name:None 175 | Parent name:None 176 | 177 | ExportToSpreadsheet:[module_num:12|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:['Export any measurements to a comma-delimited file (.csv). The measurements made for the nuclei and foci objects will be saved to separate .csv files, in addition to the per-image .csv.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 178 | Select the column delimiter:Comma (",") 179 | Add image metadata columns to your object data file?:No 180 | Add image file and folder names to your object data file?:No 181 | Select the measurements to export:No 182 | Calculate the per-image mean values for object measurements?:No 183 | Calculate the per-image median values for object measurements?:No 184 | Calculate the per-image standard deviation values for object measurements?:No 185 | Output file location:Default Output Folder| 186 | Create a GenePattern GCT file?:No 187 | Select source of sample row name:Metadata 188 | Select the image to use as the identifier:None 189 | Select the metadata to use as the identifier:None 190 | Export all measurement types?:No 191 | Press button to select measurements: 192 | Representation of Nan/Inf:NaN 193 | Add a prefix to file names?:No 194 | Filename prefix:MyExpt_ 195 | Overwrite existing files without warning?:Yes 196 | Data to export:Image 197 | Combine these object measurements with those of the previous object?:No 198 | File name:DATA.csv 199 | Use the object name for the file name?:Yes 200 | Data to export:Nuclei 201 | Combine these object measurements with those of the previous object?:No 202 | File name:DATA.csv 203 | Use the object name for the file name?:Yes 204 | Data to export:h2ax 205 | Combine these object measurements with those of the previous object?:No 206 | File name:DATA.csv 207 | Use the object name for the file name?:Yes 208 | -------------------------------------------------------------------------------- /ExampleSpeckles/README.md: -------------------------------------------------------------------------------- 1 | This pipeline shows how to identify smaller objects (foci) within larger objects (nuclei) and how to use the Relate module to establish a relationship between the two as well as perform per-object aggregate measurements (such as number of foci per nucleus). 2 | -------------------------------------------------------------------------------- /ExampleSpeckles/images/1-162hrh2ax2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleSpeckles/images/1-162hrh2ax2.tif -------------------------------------------------------------------------------- /ExampleSpeckles/images/1-162hrhoe2.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleSpeckles/images/1-162hrhoe2.tif -------------------------------------------------------------------------------- /ExampleStraightenWorms/README.md: -------------------------------------------------------------------------------- 1 | Once worms are untangled, this pipeline shows how they can be straightened and aligned with a low-resolution worm atlas to extract localized intensity measurements and compare patterns of reporter signals. 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This pipeline analyzes a time-lapse experiment to identify the cells and track them from frame to frame, which is challenging since the cells are also moving. In addition, this pipeline also can also be used to demonstrate the extraction of metadata and image grouping, in which several sequences of images are processed independently and the measurements of each are output. This is useful for analyzing time-lapse movies, for example, in which each each sequence/movie file is a distinct experimental image set. 2 | 3 | To use image grouping on this example, set the Default Input Folder to the folder containing the image subfolders Sequence1, Sequence2 and Sequence3. The LoadImages module to set to analyze the subfolders and process each one in turn. 4 | 5 | About these images: 6 | 7 | A portion of a time lapse movie of a syncytial blastoderm stage Drosophila embryo with a GFP-histone gene which renders chromatin fluorescent in live embryos. 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You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:2 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain "f.jpg") 45 | Name to assign these images:ColorFluor 46 | Name to assign these objects:Cell 47 | Select the image type:Color image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | Select the rule criteria:and (file does contain "b.jpg") 51 | Name to assign these images:ColorLung 52 | Name to assign these objects:Cell 53 | Select the image type:Color image 54 | Set intensity range from:Image metadata 55 | Maximum intensity:255.0 56 | 57 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 58 | Do you want to group your images?:No 59 | grouping metadata count:1 60 | Metadata category:None 61 | 62 | ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 63 | Select the input image:ColorLung 64 | Conversion method:Combine 65 | Image type:RGB 66 | Name the output image:GrayLung 67 | Relative weight of the red channel:1.0 68 | Relative weight of the green channel:1.0 69 | Relative weight of the blue channel:1.0 70 | Convert red to gray?:Yes 71 | Name the output image:OrigRed 72 | Convert green to gray?:Yes 73 | Name the output image:OrigGreen 74 | Convert blue to gray?:Yes 75 | Name the output image:OrigBlue 76 | Convert hue to gray?:Yes 77 | Name the output image:OrigHue 78 | Convert saturation to gray?:Yes 79 | Name the output image:OrigSaturation 80 | Convert value to gray?:Yes 81 | Name the output image:OrigValue 82 | Channel count:1 83 | Channel number:1 84 | Relative weight of the channel:1.0 85 | Image name:Channel1 86 | 87 | ColorToGray:[module_num:6|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 88 | Select the input image:ColorFluor 89 | Conversion method:Split 90 | Image type:RGB 91 | Name the output image:OrigGray 92 | Relative weight of the red channel:1.0 93 | Relative weight of the green channel:1.0 94 | Relative weight of the blue channel:1.0 95 | Convert red to gray?:No 96 | Name the output image:OrigRed 97 | Convert green to gray?:Yes 98 | Name the output image:GrayTumor 99 | Convert blue to gray?:No 100 | Name the output image:OrigBlue 101 | Convert hue to gray?:Yes 102 | Name the output image:OrigHue 103 | Convert saturation to gray?:Yes 104 | Name the output image:OrigSaturation 105 | Convert value to gray?:Yes 106 | Name the output image:OrigValue 107 | Channel count:1 108 | Channel number:1 109 | Relative weight of the channel:1.0 110 | Image name:Channel1 111 | 112 | IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 113 | Select the input image:GrayTumor 114 | Name the primary objects to be identified:tumor 115 | Typical diameter of objects, in pixel units (Min,Max):4,99999 116 | Discard objects outside the diameter range?:Yes 117 | Discard objects touching the border of the image?:Yes 118 | Method to distinguish clumped objects:Intensity 119 | Method to draw dividing lines between clumped objects:Intensity 120 | Size of smoothing filter:15 121 | Suppress local maxima that are closer than this minimum allowed distance:15 122 | Speed up by using lower-resolution image to find local maxima?:Yes 123 | Fill holes in identified objects?:After both thresholding and declumping 124 | Automatically calculate size of smoothing filter for declumping?:No 125 | Automatically calculate minimum allowed distance between local maxima?:No 126 | Handling of objects if excessive number of objects identified:Continue 127 | Maximum number of objects:500 128 | Display accepted local maxima?:No 129 | Select maxima color:Blue 130 | Use advanced settings?:Yes 131 | Threshold setting version:11 132 | Threshold strategy:Global 133 | Thresholding method:Minimum Cross-Entropy 134 | Threshold smoothing scale:1.0 135 | Threshold correction factor:1.2 136 | Lower and upper bounds on threshold:0.0,1.0 137 | Manual threshold:0.0 138 | Select the measurement to threshold with:None 139 | Two-class or three-class thresholding?:Three classes 140 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 141 | Size of adaptive window:50 142 | Lower outlier fraction:0.05 143 | Upper outlier fraction:0.05 144 | Averaging method:Mean 145 | Variance method:Standard deviation 146 | # of deviations:2.0 147 | Thresholding method:Otsu 148 | 149 | MeasureObjectSizeShape:[module_num:8|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 150 | Select object sets to measure:tumor 151 | Calculate the Zernike features?:Yes 152 | Calculate the advanced features?:No 153 | 154 | ImageMath:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 155 | Operation:Add 156 | Raise the power of the result by:1.0 157 | Multiply the result by:1.0 158 | Add to result:0.0 159 | Set values less than 0 equal to 0?:Yes 160 | Set values greater than 1 equal to 1?:Yes 161 | Replace invalid values with 0?:Yes 162 | Ignore the image masks?:No 163 | Name the output image:CombinedImage 164 | Image or measurement?:Image 165 | Select the first image:GrayLung 166 | Multiply the first image by:0.5 167 | Measurement: 168 | Image or measurement?:Image 169 | Select the second image:GrayTumor 170 | Multiply the second image by:0.5 171 | Measurement: 172 | 173 | OverlayOutlines:[module_num:10|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 174 | Display outlines on a blank image?:No 175 | Select image on which to display outlines:CombinedImage 176 | Name the output image:TumorOutline 177 | Outline display mode:Color 178 | Select method to determine brightness of outlines:Max of image 179 | How to outline:Thick 180 | Select outline color:magenta 181 | Select objects to display:tumor 182 | 183 | SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 184 | Select the type of image to save:Image 185 | Select the image to save:TumorOutline 186 | Select method for constructing file names:From image filename 187 | Select image name for file prefix:ColorFluor 188 | Enter single file name:OrigBlue 189 | Number of digits:4 190 | Append a suffix to the image file name?:Yes 191 | Text to append to the image name:_Tumors 192 | Saved file format:png 193 | Output file location:Default Output Folder| 194 | Image bit depth:8-bit integer 195 | Overwrite existing files without warning?:Yes 196 | When to save:Every cycle 197 | Record the file and path information to the saved image?:No 198 | Create subfolders in the output folder?:No 199 | Base image folder:Elsewhere...| 200 | How to save the series:T (Time) 201 | 202 | ExportToSpreadsheet:[module_num:12|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 203 | Select the column delimiter:Comma (",") 204 | Add image metadata columns to your object data file?:No 205 | Add image file and folder names to your object data file?:No 206 | Select the measurements to export:No 207 | Calculate the per-image mean values for object measurements?:No 208 | Calculate the per-image median values for object measurements?:No 209 | Calculate the per-image standard deviation values for object measurements?:No 210 | Output file location:Default Output Folder| 211 | Create a GenePattern GCT file?:No 212 | Select source of sample row name:Metadata 213 | Select the image to use as the identifier:None 214 | Select the metadata to use as the identifier:None 215 | Export all measurement types?:Yes 216 | Press button to select measurements: 217 | Representation of Nan/Inf:NaN 218 | Add a prefix to file names?:No 219 | Filename prefix:MyExpt_ 220 | Overwrite existing files without warning?:Yes 221 | -------------------------------------------------------------------------------- /ExampleTumor/README.md: -------------------------------------------------------------------------------- 1 | An excised mouse lung has GFP labeled tumors. These tumors can be counted and quantified. 2 | 3 | Note, the source images are jpegs. jpegs should be avoided, because information is lost through image compression. While jpegs can still be used in a pipeline the results may be noisier than they otherwise would have been. 4 | -------------------------------------------------------------------------------- /ExampleTumor/images/30-2A1b.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleTumor/images/30-2A1b.jpg -------------------------------------------------------------------------------- /ExampleTumor/images/30-2A1f.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleTumor/images/30-2A1f.jpg -------------------------------------------------------------------------------- /ExampleUntangleWorms/README.md: -------------------------------------------------------------------------------- 1 | In this pipeline, we identify individual worms and extract shape and intensity measurements. Worm untangling requires a worm model, which is provided together with the pipeline. If adjusting the pipeline to fit your own data, worm detection will likely improve by creating a new worm model based on your own image data. 2 | -------------------------------------------------------------------------------- /ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C01_w1_10E01AFB-34C4-416E-A9D3-51B90AB53728.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C01_w1_10E01AFB-34C4-416E-A9D3-51B90AB53728.tif -------------------------------------------------------------------------------- /ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C01_w2_CB2F18CD-EDF0-4BCD-98CF-3A07E5A582FF.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C01_w2_CB2F18CD-EDF0-4BCD-98CF-3A07E5A582FF.tif -------------------------------------------------------------------------------- /ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C20_w1_0F5A41CB-2646-49E5-9281-F5B1F655B7BC.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C20_w1_0F5A41CB-2646-49E5-9281-F5B1F655B7BC.tif -------------------------------------------------------------------------------- /ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C20_w2_5F64C597-735E-435D-B49C-2A07B9D6DFC3.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWorms/images/1649_1109_0003_Amp5-1_B_20070424_C20_w2_5F64C597-735E-435D-B49C-2A07B9D6DFC3.tif -------------------------------------------------------------------------------- /ExampleUntangleWormsBrightField/README.md: -------------------------------------------------------------------------------- 1 | This pipeline detects individual worms by worm untangling and finds sub-objects (fatty regions stained with oil red O) within the worms. Using bright-field data only, it detects fatty regions by intensity thresholding in a single image channel and relates the fatty regions to individual worms. This enables detection of rare phenotypes in heterogeneous populations, phenotypes that would be missed if population averages were observed. More data can be found on the BBBC. 2 | -------------------------------------------------------------------------------- /ExampleUntangleWormsBrightField/images/WT_orig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWormsBrightField/images/WT_orig.png -------------------------------------------------------------------------------- /ExampleUntangleWormsBrightField/images/fat_orig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleUntangleWormsBrightField/images/fat_orig.png -------------------------------------------------------------------------------- /ExampleVitraImages/README.md: -------------------------------------------------------------------------------- 1 | In this human cytoplasm-nucleus translocation assay, learn how to load a previously calculated illumination correction function for two separate channels, measure protein content in the nucleus and cytoplasm, and calculate the ratio as a measure of translocation. This is a clumpy cell type, so studying the settings in primary object identification may be helpful for users interested in the more advanced options that module offers. More about these images can be found at the [BBBC](https://data.broadinstitute.org/bbbc/BBBC014/). 2 | -------------------------------------------------------------------------------- /ExampleVitraImages/images/Channel 1-01-A-01-00.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/Channel 1-01-A-01-00.tif -------------------------------------------------------------------------------- /ExampleVitraImages/images/Channel 1-12-A-12-00.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/Channel 1-12-A-12-00.tif -------------------------------------------------------------------------------- /ExampleVitraImages/images/Channel 2-01-A-01-00.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/Channel 2-01-A-01-00.tif -------------------------------------------------------------------------------- /ExampleVitraImages/images/Channel 2-12-A-12-00.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/Channel 2-12-A-12-00.tif -------------------------------------------------------------------------------- /ExampleVitraImages/images/VitraChannel1ILLUM.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/VitraChannel1ILLUM.npy -------------------------------------------------------------------------------- /ExampleVitraImages/images/VitraChannel2ILLUM.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleVitraImages/images/VitraChannel2ILLUM.npy -------------------------------------------------------------------------------- /ExampleWoundHealing/ExampleWoundHealing.cppipe: -------------------------------------------------------------------------------- 1 | CellProfiler Pipeline: http://www.cellprofiler.org 2 | Version:5 3 | DateRevision:400 4 | GitHash: 5 | ModuleCount:9 6 | HasImagePlaneDetails:False 7 | 8 | Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 9 | : 10 | Filter images?:Images only 11 | Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") 12 | 13 | Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 14 | Extract metadata?:No 15 | Metadata data type:Text 16 | Metadata types:{} 17 | Extraction method count:1 18 | Metadata extraction method:Extract from file/folder names 19 | Metadata source:File name 20 | Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) 21 | Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ 22 | Extract metadata from:All images 23 | Select the filtering criteria:and (file does contain "") 24 | Metadata file location:Elsewhere...| 25 | Match file and image metadata:[] 26 | Use case insensitive matching?:No 27 | Metadata file name: 28 | Does cached metadata exist?:No 29 | 30 | NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', 'â\x80\x94', 'Load the images by matching files in the folder against the unique text pattern â\x80\x98.JPGâ\x80\x99']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 31 | Assign a name to:Images matching rules 32 | Select the image type:Grayscale image 33 | Name to assign these images:DNA 34 | Match metadata:[] 35 | Image set matching method:Order 36 | Set intensity range from:Image metadata 37 | Assignments count:1 38 | Single images count:0 39 | Maximum intensity:255.0 40 | Process as 3D?:No 41 | Relative pixel spacing in X:1.0 42 | Relative pixel spacing in Y:1.0 43 | Relative pixel spacing in Z:1.0 44 | Select the rule criteria:and (file does contain ".JPG") 45 | Name to assign these images:OrigColor 46 | Name to assign these objects:Cell 47 | Select the image type:Color image 48 | Set intensity range from:Image metadata 49 | Maximum intensity:255.0 50 | 51 | Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 52 | Do you want to group your images?:No 53 | grouping metadata count:1 54 | Metadata category:None 55 | 56 | ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Combine the color image into a grayscale image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 57 | Select the input image:OrigColor 58 | Conversion method:Combine 59 | Image type:RGB 60 | Name the output image:OrigGray 61 | Relative weight of the red channel:1.0 62 | Relative weight of the green channel:1.0 63 | Relative weight of the blue channel:1.0 64 | Convert red to gray?:Yes 65 | Name the output image:OrigRed 66 | Convert green to gray?:Yes 67 | Name the output image:OrigGreen 68 | Convert blue to gray?:Yes 69 | Name the output image:OrigBlue 70 | Convert hue to gray?:Yes 71 | Name the output image:OrigHue 72 | Convert saturation to gray?:Yes 73 | Name the output image:OrigSaturation 74 | Convert value to gray?:Yes 75 | Name the output image:OrigValue 76 | Channel count:1 77 | Channel number:1 78 | Relative weight of the channel:1.0 79 | Image name:Channel1 80 | 81 | Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Smooth the image using a Gaussian filter.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 82 | Select the input image:OrigGray 83 | Name the output image:Corrected 84 | Select smoothing method:Gaussian Filter 85 | Calculate artifact diameter automatically?:No 86 | Typical artifact diameter:20 87 | Edge intensity difference:0.1 88 | Clip intensities to 0 and 1?:Yes 89 | 90 | IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify the tissue region using three-class Otsu.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 91 | Select the input image:Corrected 92 | Name the primary objects to be identified:Tissue 93 | Typical diameter of objects, in pixel units (Min,Max):10,40 94 | Discard objects outside the diameter range?:No 95 | Discard objects touching the border of the image?:No 96 | Method to distinguish clumped objects:None 97 | Method to draw dividing lines between clumped objects:Intensity 98 | Size of smoothing filter:10 99 | Suppress local maxima that are closer than this minimum allowed distance:7.0 100 | Speed up by using lower-resolution image to find local maxima?:Yes 101 | Fill holes in identified objects?:Never 102 | Automatically calculate size of smoothing filter for declumping?:Yes 103 | Automatically calculate minimum allowed distance between local maxima?:Yes 104 | Handling of objects if excessive number of objects identified:Continue 105 | Maximum number of objects:500 106 | Display accepted local maxima?:No 107 | Select maxima color:Blue 108 | Use advanced settings?:Yes 109 | Threshold setting version:11 110 | Threshold strategy:Global 111 | Thresholding method:Otsu 112 | Threshold smoothing scale:1.3488 113 | Threshold correction factor:0.95 114 | Lower and upper bounds on threshold:0.0,1.0 115 | Manual threshold:0.0 116 | Select the measurement to threshold with:None 117 | Two-class or three-class thresholding?:Three classes 118 | Assign pixels in the middle intensity class to the foreground or the background?:Foreground 119 | Size of adaptive window:50 120 | Lower outlier fraction:0.05 121 | Upper outlier fraction:0.05 122 | Averaging method:Mean 123 | Variance method:Standard deviation 124 | # of deviations:2.0 125 | Thresholding method:Otsu 126 | 127 | MeasureImageAreaOccupied:[module_num:8|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure the area occupied by the tissue region.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 128 | Measure the area occupied by:Objects 129 | Select binary images to measure: 130 | Select object sets to measure:Tissue 131 | 132 | ExportToSpreadsheet:[module_num:9|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:['Export any measurements to a comma-delimited file (.csv). Since the tissue area is an image measurement, it is included in the per-image file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] 133 | Select the column delimiter:Comma (",") 134 | Add image metadata columns to your object data file?:No 135 | Add image file and folder names to your object data file?:No 136 | Select the measurements to export:No 137 | Calculate the per-image mean values for object measurements?:Yes 138 | Calculate the per-image median values for object measurements?:No 139 | Calculate the per-image standard deviation values for object measurements?:No 140 | Output file location:Default Output Folder| 141 | Create a GenePattern GCT file?:No 142 | Select source of sample row name:Metadata 143 | Select the image to use as the identifier:None 144 | Select the metadata to use as the identifier:None 145 | Export all measurement types?:No 146 | Press button to select measurements: 147 | Representation of Nan/Inf:NaN 148 | Add a prefix to file names?:No 149 | Filename prefix:MyExpt_ 150 | Overwrite existing files without warning?:Yes 151 | Data to export:Image 152 | Combine these object measurements with those of the previous object?:No 153 | File name:DATA.csv 154 | Use the object name for the file name?:Yes 155 | -------------------------------------------------------------------------------- /ExampleWoundHealing/README.md: -------------------------------------------------------------------------------- 1 | In this example, cells are grown as a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the area occupied by the tissue sample. 2 | -------------------------------------------------------------------------------- /ExampleWoundHealing/images/DMSO_B5_t0.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleWoundHealing/images/DMSO_B5_t0.JPG -------------------------------------------------------------------------------- /ExampleWoundHealing/images/DMSO_B5_t24.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleWoundHealing/images/DMSO_B5_t24.JPG -------------------------------------------------------------------------------- /ExampleYeastColonies/85-Bray_CurrentProtocols_2015.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleYeastColonies/85-Bray_CurrentProtocols_2015.pdf -------------------------------------------------------------------------------- /ExampleYeastColonies/README.md: -------------------------------------------------------------------------------- 1 | Ref: Bray, Current Protocols 2015, Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images 2 | Note that the PDF is for a previous version of CellProfiler. 3 | 4 | A protocol for the automated counting of yeast colonies grown on agar plates. 5 | -------------------------------------------------------------------------------- /ExampleYeastColonies/images/6-1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleYeastColonies/images/6-1.jpg -------------------------------------------------------------------------------- /ExampleYeastColonies/images/PlateTemplate.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleYeastColonies/images/PlateTemplate.png -------------------------------------------------------------------------------- /ExampleYeastPatches/README.md: -------------------------------------------------------------------------------- 1 | Images from Aaron Gitler, Lindquist lab, Whitehead Institute 2 | -------------------------------------------------------------------------------- /ExampleYeastPatches/images/1832-48hours-gal-1.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CellProfiler/examples/4972b59e670a4ae96c3d453803c92eeff378d054/ExampleYeastPatches/images/1832-48hours-gal-1.JPG -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2017, CellProfiler 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | * Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: clean test 2 | 3 | clean: 4 | rm -rf ExampleCometAssay/output 5 | rm -rf ExampleFly/output 6 | rm -rf ExampleHuman/output 7 | rm -rf ExamplePercentPositive/output 8 | rm -rf ExampleTumor/output 9 | rm -rf ExampleYeastColonies/output 10 | rm -rf ExampleYeastPatches/output 11 | rm -rf ExampleColocalization/output 12 | rm -rf ExampleIlluminationCorrection/output_all 13 | rm -rf ExampleIlluminationCorrection/output_each 14 | rm -rf ExampleIlluminationCorrection/output2 15 | rm -rf ExampleIlluminationCorrection/output3 16 | rm -rf ExampleNeighbors/output 17 | rm -rf ExampleSpeckles/output 18 | rm -rf ExampleTrackObjects/output 19 | rm -rf ExampleWoundHealing/output 20 | rm -rf ExampleImagingFlowCytometryObjectsInGrid/output 21 | rm -rf ExampleVitraImages/output 22 | 23 | test: 24 | cellprofiler -c -r -p ExampleCometAssay/ExampleCometAssay.cppipe \ 25 | -i ExampleCometAssay/images \ 26 | -o ExampleCometAssay/output \ 27 | -d ExampleCometAssay/output/done 28 | 29 | cellprofiler -c -r -p ExampleFly/ExampleFly.cppipe \ 30 | -i ExampleFly/images \ 31 | -o ExampleFly/output \ 32 | -d ExampleFly/output/done 33 | 34 | cellprofiler -c -r -p ExampleHuman/ExampleHuman.cppipe \ 35 | -i ExampleHuman/images \ 36 | -o ExampleHuman/output \ 37 | -d ExampleHuman/output/done 38 | 39 | cellprofiler -c -r -p ExamplePercentPositive/ExamplePercentPositive.cppipe \ 40 | -i ExamplePercentPositive/images \ 41 | -o ExamplePercentPositive/output \ 42 | -d ExamplePercentPositive/output/done 43 | 44 | cellprofiler -c -r -p ExampleTumor/ExampleTumor.cppipe \ 45 | -i ExampleTumor/images \ 46 | -o ExampleTumor/output \ 47 | -d ExampleTumor/output/done 48 | 49 | cellprofiler -c -r -p ExampleYeastColonies/ExampleYeastColonies.cppipe \ 50 | -i ExampleYeastColonies/images \ 51 | -o ExampleYeastColonies/output \ 52 | -d ExampleYeastColonies/output/done 53 | 54 | cellprofiler -c -r -p ExampleYeastPatches/ExampleYeastPatches.cppipe \ 55 | -i ExampleYeastPatches/images \ 56 | -o ExampleYeastPatches/output \ 57 | -d ExampleYeastPatches/output/done 58 | 59 | cellprofiler -c -r -p ExampleColocalization/ExampleColocalization.cppipe \ 60 | -i ExampleColocalization/images \ 61 | -o ExampleColocalization/output \ 62 | -d ExampleColocalization/output/done 63 | 64 | cellprofiler -c -r -p ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example1_AllMethod.cppipe \ 65 | -i ExampleIlluminationCorrection/images \ 66 | -o ExampleIlluminationCorrection/output_all \ 67 | -d ExampleIlluminationCorrection/output_all/done 68 | 69 | cellprofiler -c -r -p ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example1_EachMethod.cppipe \ 70 | -i ExampleIlluminationCorrection/images \ 71 | -o ExampleIlluminationCorrection/output_each \ 72 | -d ExampleIlluminationCorrection/output_each/done 73 | 74 | cellprofiler -c -r -p ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example2.cppipe \ 75 | -i ExampleIlluminationCorrection/images \ 76 | -o ExampleIlluminationCorrection/output2 \ 77 | -d ExampleIlluminationCorrection/output2/done 78 | 79 | cellprofiler -c -r -p ExampleIlluminationCorrection/ExampleIlluminationCorrection_Example3.cppipe \ 80 | -i ExampleIlluminationCorrection/images \ 81 | -o ExampleIlluminationCorrection/output3 \ 82 | -d ExampleIlluminationCorrection/output3/done 83 | 84 | cellprofiler -c -r -p ExampleNeighbors/ExampleNeighbors.cppipe \ 85 | -i ExampleNeighbors/images \ 86 | -o ExampleNeighbors/output \ 87 | -d ExampleNeighbors/output/done 88 | 89 | cellprofiler -c -r -p ExampleSpeckles/ExampleSpeckles.cppipe \ 90 | -i ExampleSpeckles/images \ 91 | -o ExampleSpeckles/output \ 92 | -d ExampleSpeckles/output/done 93 | 94 | cellprofiler -c -r -p ExampleTrackObjects/ExampleTrackObjects.cppipe \ 95 | -i ExampleTrackObjects/images \ 96 | -o ExampleTrackObjects/output \ 97 | -d ExampleTrackObjects/output/done 98 | 99 | cellprofiler -c -r -p ExampleWoundHealing/ExampleWoundHealing.cppipe \ 100 | -i ExampleWoundHealing/images \ 101 | -o ExampleWoundHealing/output \ 102 | -d ExampleWoundHealing/output/done 103 | 104 | cellprofiler -c -r -p ExampleImagingFlowCytometryObjectsInGrid/ExampleImagingFlowCytometryObjectsInGrid.cppipe \ 105 | -i ExampleImagingFlowCytometryObjectsInGrid/images \ 106 | -o ExampleImagingFlowCytometryObjectsInGrid/output \ 107 | -d ExampleImagingFlowCytometryObjectsInGrid/output/done 108 | 109 | cellprofiler -c -r -p ExampleUntangleWorms/ExampleUntangleWorms.cppipe \ 110 | -i ExampleUntangleWorms/images \ 111 | -o ExampleUntangleWorms/output \ 112 | -d ExampleUntangleWorms/output/done 113 | 114 | cellprofiler -c -r -p ExampleStraightenWorms/ExampleUntangleAndStraightenWorms.cppipe \ 115 | -i ExampleStraightenWorms/images \ 116 | -o ExampleStraightenWorms/output \ 117 | -d ExampleStraightenWorms/output/done 118 | 119 | cellprofiler -c -r -p ExampleUntangleWormsBrightField/ExampleUntangleWormsBrightField.cppipe \ 120 | -i ExampleUntangleWormsBrightField/images \ 121 | -o ExampleUntangleWormsBrightField/output \ 122 | -d ExampleUntangleWormsBrightField/output/done 123 | 124 | cellprofiler -c -r -p ExampleVitraImages/ExampleVitra.cppipe \ 125 | -i ExampleVitraImages/images \ 126 | -o ExampleVitraImages/output \ 127 | -d ExampleVitraImages/output/done 128 | 129 | @if [ $$(cat ExampleCometAssay/output/done) = Failure ] || \ 130 | [ $$(cat ExampleFly/output/done) = Failure ] || \ 131 | [ $$(cat ExampleHuman/output/done) = Failure ] || \ 132 | [ $$(cat ExamplePercentPositive/output/done) = Failure ] || \ 133 | [ $$(cat ExampleTumor/output/done) = Failure ] || \ 134 | [ $$(cat ExampleYeastColonies/output/done) = Failure ] || \ 135 | [ $$(cat ExampleYeastPatches/output/done) = Failure ] || \ 136 | [ $$(cat ExampleColocalization/output/done) = Failure ] || \ 137 | [ $$(cat ExampleIlluminationCorrection/output_all/done) = Failure ] || \ 138 | [ $$(cat ExampleIlluminationCorrection/output_each/done) = Failure ] || \ 139 | [ $$(cat ExampleIlluminationCorrection/output2/done) = Failure ] || \ 140 | [ $$(cat ExampleIlluminationCorrection/output3/done) = Failure ] || \ 141 | [ $$(cat ExampleNeighbors/output/done) = Failure ] || \ 142 | [ $$(cat ExampleSpeckles/output/done) = Failure ] || \ 143 | [ $$(cat ExampleTrackObjects/output/done) = Failure ] || \ 144 | [ $$(cat ExampleWoundHealing/output/done) = Failure ] || \ 145 | [ $$(cat ExampleUntangleWorms/output/done) = Failure ] || \ 146 | [ $$(cat ExampleStraightenWorms/output/done) = Failure ] || \ 147 | [ $$(cat ExampleUntangleWormsBrightField/output/done) = Failure ] || \ 148 | [ $$(cat ExampleImagingFlowCytometryObjectsInGrid/output/done) = Failure ] || \ 149 | [ $$(cat ExampleVitraImages/output/done) = Failure ]; then \ 150 | false; \ 151 | else true; fi 152 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # examples 2 | Example CellProfiler pipelines. 3 | --------------------------------------------------------------------------------