├── tests ├── __init__.py ├── context.py └── test_core.py ├── doc ├── imgs │ ├── GLCM.png │ ├── GLSZM.png │ ├── NGTDM.png │ ├── example_img.png │ ├── glcm_example.png │ ├── glha_example.png │ ├── glszm_example.png │ └── ngtdm_example.png └── Gallery.md ├── data └── PA0 │ └── ST0 │ ├── SE2 │ └── IM0 │ ├── SE0 │ ├── IM-0002-0001.dcm │ ├── IM-0002-0002.dcm │ ├── IM-0002-0003.dcm │ ├── IM-0002-0004.dcm │ ├── IM-0002-0005.dcm │ ├── IM-0002-0006.dcm │ ├── IM-0002-0007.dcm │ ├── IM-0002-0008.dcm │ ├── IM-0002-0009.dcm │ ├── IM-0002-0010.dcm │ ├── IM-0002-0011.dcm │ ├── IM-0002-0012.dcm │ ├── IM-0002-0013.dcm │ ├── IM-0002-0014.dcm │ ├── IM-0002-0015.dcm │ ├── IM-0002-0016.dcm │ ├── IM-0002-0017.dcm │ ├── IM-0002-0018.dcm │ ├── IM-0002-0019.dcm │ ├── IM-0002-0020.dcm │ ├── IM-0002-0021.dcm │ ├── IM-0002-0022.dcm │ ├── IM-0002-0023.dcm │ ├── IM-0002-0024.dcm │ ├── IM-0002-0025.dcm │ ├── IM-0002-0026.dcm │ ├── IM-0002-0027.dcm │ ├── IM-0002-0028.dcm │ ├── IM-0002-0029.dcm │ ├── IM-0002-0030.dcm │ ├── IM-0002-0031.dcm │ ├── IM-0002-0032.dcm │ ├── IM-0002-0033.dcm │ ├── IM-0002-0034.dcm │ ├── IM-0002-0035.dcm │ ├── IM-0002-0036.dcm │ ├── IM-0002-0037.dcm │ ├── IM-0002-0038.dcm │ ├── IM-0002-0039.dcm │ ├── IM-0002-0040.dcm │ ├── IM-0002-0041.dcm │ ├── IM-0002-0042.dcm │ ├── IM-0002-0043.dcm │ ├── IM-0002-0044.dcm │ ├── IM-0002-0045.dcm │ ├── IM-0002-0046.dcm │ ├── IM-0002-0047.dcm │ ├── IM-0002-0048.dcm │ ├── IM-0002-0049.dcm │ ├── IM-0002-0050.dcm │ ├── IM-0002-0051.dcm │ ├── IM-0002-0052.dcm │ ├── IM-0002-0053.dcm │ ├── IM-0002-0054.dcm │ ├── IM-0002-0055.dcm │ ├── IM-0002-0056.dcm │ ├── IM-0002-0057.dcm │ ├── IM-0002-0058.dcm │ ├── IM-0002-0059.dcm │ ├── IM-0002-0060.dcm │ ├── IM-0002-0061.dcm │ ├── IM-0002-0062.dcm │ ├── IM-0002-0063.dcm │ ├── IM-0002-0064.dcm │ ├── IM-0002-0065.dcm │ ├── IM-0002-0066.dcm │ ├── IM-0002-0067.dcm │ ├── IM-0002-0068.dcm │ ├── IM-0002-0069.dcm │ ├── IM-0002-0070.dcm │ ├── IM-0002-0071.dcm │ ├── IM-0002-0072.dcm │ ├── IM-0002-0073.dcm │ ├── IM-0002-0074.dcm │ ├── IM-0002-0075.dcm │ ├── IM-0002-0076.dcm │ ├── IM-0002-0077.dcm │ ├── IM-0002-0078.dcm │ ├── IM-0002-0079.dcm │ ├── IM-0002-0080.dcm │ ├── IM-0002-0081.dcm │ ├── IM-0002-0082.dcm │ ├── IM-0002-0083.dcm │ ├── IM-0002-0084.dcm │ ├── IM-0002-0085.dcm │ ├── IM-0002-0086.dcm │ ├── IM-0002-0087.dcm │ ├── IM-0002-0088.dcm │ ├── IM-0002-0089.dcm │ ├── IM-0002-0090.dcm │ ├── IM-0002-0091.dcm │ ├── IM-0002-0092.dcm │ ├── IM-0002-0093.dcm │ ├── IM-0002-0094.dcm │ ├── IM-0002-0095.dcm │ ├── IM-0002-0096.dcm │ ├── IM-0002-0097.dcm │ ├── IM-0002-0098.dcm │ ├── IM-0002-0099.dcm │ ├── IM-0002-0100.dcm │ ├── IM-0002-0101.dcm │ ├── IM-0002-0102.dcm │ ├── IM-0002-0103.dcm │ ├── IM-0002-0104.dcm │ ├── IM-0002-0105.dcm │ ├── IM-0002-0106.dcm │ ├── IM-0002-0107.dcm │ ├── IM-0002-0108.dcm │ ├── IM-0002-0109.dcm │ ├── IM-0002-0110.dcm │ ├── IM-0002-0111.dcm │ ├── IM-0002-0112.dcm │ ├── IM-0002-0113.dcm │ ├── IM-0002-0114.dcm │ ├── IM-0002-0115.dcm │ ├── IM-0002-0116.dcm │ ├── IM-0002-0117.dcm │ ├── IM-0002-0118.dcm │ ├── IM-0002-0119.dcm │ ├── IM-0002-0120.dcm │ ├── IM-0002-0121.dcm │ ├── IM-0002-0122.dcm │ ├── IM-0002-0123.dcm │ ├── IM-0002-0124.dcm │ ├── IM-0002-0125.dcm │ ├── IM-0002-0126.dcm │ ├── IM-0002-0127.dcm │ ├── IM-0002-0128.dcm │ ├── IM-0002-0129.dcm │ ├── IM-0002-0130.dcm │ ├── IM-0002-0131.dcm │ ├── IM-0002-0132.dcm │ ├── IM-0002-0133.dcm │ ├── IM-0002-0134.dcm │ ├── IM-0002-0135.dcm │ ├── IM-0002-0136.dcm │ ├── IM-0002-0137.dcm │ ├── IM-0002-0138.dcm │ ├── IM-0002-0139.dcm │ ├── IM-0002-0140.dcm │ ├── IM-0002-0141.dcm │ ├── IM-0002-0142.dcm │ ├── IM-0002-0143.dcm │ ├── IM-0002-0144.dcm │ ├── IM-0002-0145.dcm │ ├── IM-0002-0146.dcm │ ├── IM-0002-0147.dcm │ ├── IM-0002-0148.dcm │ ├── IM-0002-0149.dcm │ ├── IM-0002-0150.dcm │ ├── IM-0002-0151.dcm │ ├── IM-0002-0152.dcm │ ├── IM-0002-0153.dcm │ ├── IM-0002-0154.dcm │ ├── IM-0002-0155.dcm │ ├── IM-0002-0156.dcm │ ├── IM-0002-0157.dcm │ ├── IM-0002-0158.dcm │ ├── IM-0002-0159.dcm │ ├── IM-0002-0160.dcm │ ├── IM-0002-0161.dcm │ ├── IM-0002-0162.dcm │ ├── IM-0002-0163.dcm │ ├── IM-0002-0164.dcm │ ├── IM-0002-0165.dcm │ ├── IM-0002-0166.dcm │ ├── IM-0002-0167.dcm │ ├── IM-0002-0168.dcm │ ├── IM-0002-0169.dcm │ ├── IM-0002-0170.dcm │ ├── IM-0002-0171.dcm │ ├── IM-0002-0172.dcm │ ├── IM-0002-0173.dcm │ ├── IM-0002-0174.dcm │ ├── IM-0002-0175.dcm │ ├── IM-0002-0176.dcm │ ├── IM-0002-0177.dcm │ ├── IM-0002-0178.dcm │ ├── IM-0002-0179.dcm │ ├── IM-0002-0180.dcm │ ├── IM-0002-0181.dcm │ ├── IM-0002-0182.dcm │ ├── IM-0002-0183.dcm │ ├── IM-0002-0184.dcm │ ├── IM-0002-0185.dcm │ ├── IM-0002-0186.dcm │ ├── IM-0002-0187.dcm │ ├── IM-0002-0188.dcm │ ├── IM-0002-0189.dcm │ ├── IM-0002-0190.dcm │ ├── IM-0002-0191.dcm │ ├── IM-0002-0192.dcm │ ├── IM-0002-0193.dcm │ ├── IM-0002-0194.dcm │ ├── IM-0002-0195.dcm │ ├── IM-0002-0196.dcm │ ├── IM-0002-0197.dcm │ ├── IM-0002-0198.dcm │ ├── IM-0002-0199.dcm │ ├── IM-0002-0200.dcm │ ├── IM-0002-0201.dcm │ ├── IM-0002-0202.dcm │ ├── IM-0002-0203.dcm │ ├── IM-0002-0204.dcm │ ├── IM-0002-0205.dcm │ ├── IM-0002-0206.dcm │ ├── IM-0002-0207.dcm │ ├── IM-0002-0208.dcm │ ├── IM-0002-0209.dcm │ ├── IM-0002-0210.dcm │ ├── IM-0002-0211.dcm │ ├── IM-0002-0212.dcm │ ├── IM-0002-0213.dcm │ ├── IM-0002-0214.dcm │ ├── IM-0002-0215.dcm │ ├── IM-0002-0216.dcm │ ├── IM-0002-0217.dcm │ ├── IM-0002-0218.dcm │ ├── IM-0002-0219.dcm │ ├── IM-0002-0220.dcm │ ├── IM-0002-0221.dcm │ ├── IM-0002-0222.dcm │ ├── IM-0002-0223.dcm │ ├── IM-0002-0224.dcm │ ├── IM-0002-0225.dcm │ ├── IM-0002-0226.dcm │ ├── IM-0002-0227.dcm │ ├── IM-0002-0228.dcm │ ├── IM-0002-0229.dcm │ ├── IM-0002-0230.dcm │ ├── IM-0002-0231.dcm │ ├── IM-0002-0232.dcm │ ├── IM-0002-0233.dcm │ ├── IM-0002-0234.dcm │ ├── IM-0002-0235.dcm │ ├── IM-0002-0236.dcm │ ├── IM-0002-0237.dcm │ ├── IM-0002-0238.dcm │ ├── IM-0002-0239.dcm │ ├── IM-0002-0240.dcm │ ├── IM-0002-0241.dcm │ ├── IM-0002-0242.dcm │ ├── IM-0002-0243.dcm │ ├── IM-0002-0244.dcm │ ├── IM-0002-0245.dcm │ ├── IM-0002-0246.dcm │ ├── IM-0002-0247.dcm │ ├── IM-0002-0248.dcm │ ├── IM-0002-0249.dcm │ ├── IM-0002-0250.dcm │ ├── IM-0002-0251.dcm │ ├── IM-0002-0252.dcm │ ├── IM-0002-0253.dcm │ ├── IM-0002-0254.dcm │ ├── IM-0002-0255.dcm │ ├── IM-0002-0256.dcm │ ├── IM-0002-0257.dcm │ ├── IM-0002-0258.dcm │ ├── IM-0002-0259.dcm │ ├── IM-0002-0260.dcm │ ├── IM-0002-0261.dcm │ ├── IM-0002-0262.dcm │ ├── IM-0002-0263.dcm │ ├── IM-0002-0264.dcm │ ├── IM-0002-0265.dcm │ ├── IM-0002-0266.dcm │ ├── IM-0002-0267.dcm │ ├── IM-0002-0268.dcm │ ├── IM-0002-0269.dcm │ ├── IM-0002-0270.dcm │ ├── IM-0002-0271.dcm │ ├── IM-0002-0272.dcm │ ├── IM-0002-0273.dcm │ ├── IM-0002-0274.dcm │ ├── IM-0002-0275.dcm │ ├── IM-0002-0276.dcm │ ├── IM-0002-0277.dcm │ ├── IM-0002-0278.dcm │ ├── IM-0002-0279.dcm │ ├── IM-0002-0280.dcm │ ├── IM-0002-0281.dcm │ ├── IM-0002-0282.dcm │ ├── IM-0002-0283.dcm │ ├── IM-0002-0284.dcm │ ├── IM-0002-0285.dcm │ ├── IM-0002-0286.dcm │ ├── IM-0002-0287.dcm │ ├── IM-0002-0288.dcm │ ├── IM-0002-0289.dcm │ ├── IM-0002-0290.dcm │ ├── IM-0002-0291.dcm │ ├── IM-0002-0292.dcm │ ├── IM-0002-0293.dcm │ ├── IM-0002-0294.dcm │ ├── IM-0002-0295.dcm │ ├── IM-0002-0296.dcm │ ├── IM-0002-0297.dcm │ ├── IM-0002-0298.dcm │ ├── IM-0002-0299.dcm │ ├── IM-0002-0300.dcm │ ├── IM-0002-0301.dcm │ ├── IM-0002-0302.dcm │ ├── IM-0002-0303.dcm │ ├── IM-0002-0304.dcm │ ├── IM-0002-0305.dcm │ ├── IM-0002-0306.dcm │ ├── IM-0002-0307.dcm │ ├── IM-0002-0308.dcm │ ├── IM-0002-0309.dcm │ ├── IM-0002-0310.dcm │ ├── IM-0002-0311.dcm │ ├── IM-0002-0312.dcm │ ├── IM-0002-0313.dcm │ ├── IM-0002-0314.dcm │ ├── IM-0002-0315.dcm │ ├── IM-0002-0316.dcm │ ├── IM-0002-0317.dcm │ ├── IM-0002-0318.dcm │ ├── IM-0002-0319.dcm │ ├── IM-0002-0320.dcm │ ├── IM-0002-0321.dcm │ ├── IM-0002-0322.dcm │ ├── IM-0002-0323.dcm │ ├── IM-0002-0324.dcm │ ├── IM-0002-0325.dcm │ ├── IM-0002-0326.dcm │ ├── IM-0002-0327.dcm │ ├── IM-0002-0328.dcm │ ├── IM-0002-0329.dcm │ ├── IM-0002-0330.dcm │ ├── IM-0002-0331.dcm │ ├── IM-0002-0332.dcm │ ├── IM-0002-0333.dcm │ ├── IM-0002-0334.dcm │ └── IM-0002-0335.dcm │ └── SE1 │ ├── IM-0001-0001.dcm │ ├── IM-0001-0002.dcm │ ├── IM-0001-0003.dcm │ ├── IM-0001-0004.dcm │ ├── IM-0001-0005.dcm │ ├── IM-0001-0006.dcm │ ├── IM-0001-0007.dcm │ ├── IM-0001-0008.dcm │ ├── IM-0001-0009.dcm │ ├── IM-0001-0010.dcm │ ├── IM-0001-0011.dcm │ ├── IM-0001-0012.dcm │ ├── IM-0001-0013.dcm │ ├── IM-0001-0014.dcm │ ├── IM-0001-0015.dcm │ ├── IM-0001-0016.dcm │ ├── IM-0001-0017.dcm │ ├── IM-0001-0018.dcm │ ├── IM-0001-0019.dcm │ ├── IM-0001-0020.dcm │ ├── IM-0001-0021.dcm │ ├── IM-0001-0022.dcm │ ├── IM-0001-0023.dcm │ ├── IM-0001-0024.dcm │ ├── IM-0001-0025.dcm │ ├── IM-0001-0026.dcm │ ├── IM-0001-0027.dcm │ ├── IM-0001-0028.dcm │ ├── IM-0001-0029.dcm │ ├── IM-0001-0030.dcm │ ├── IM-0001-0031.dcm │ ├── IM-0001-0032.dcm │ ├── IM-0001-0033.dcm │ ├── IM-0001-0034.dcm │ ├── IM-0001-0035.dcm │ ├── IM-0001-0036.dcm │ ├── IM-0001-0037.dcm │ ├── IM-0001-0038.dcm │ ├── IM-0001-0039.dcm │ ├── IM-0001-0040.dcm │ ├── IM-0001-0041.dcm │ ├── IM-0001-0042.dcm │ ├── IM-0001-0043.dcm │ ├── IM-0001-0044.dcm │ ├── IM-0001-0045.dcm │ ├── IM-0001-0046.dcm │ ├── IM-0001-0047.dcm │ ├── IM-0001-0048.dcm │ ├── IM-0001-0049.dcm │ ├── IM-0001-0050.dcm │ ├── IM-0001-0051.dcm │ ├── IM-0001-0052.dcm │ ├── IM-0001-0053.dcm │ ├── IM-0001-0054.dcm │ ├── IM-0001-0055.dcm │ ├── IM-0001-0056.dcm │ ├── IM-0001-0057.dcm │ ├── IM-0001-0058.dcm │ ├── IM-0001-0059.dcm │ ├── IM-0001-0060.dcm │ ├── IM-0001-0061.dcm │ ├── IM-0001-0062.dcm │ ├── IM-0001-0063.dcm │ ├── IM-0001-0064.dcm │ ├── IM-0001-0065.dcm │ ├── IM-0001-0066.dcm │ ├── IM-0001-0067.dcm │ ├── IM-0001-0068.dcm │ ├── IM-0001-0069.dcm │ ├── IM-0001-0070.dcm │ ├── IM-0001-0071.dcm │ ├── IM-0001-0072.dcm │ ├── IM-0001-0073.dcm │ ├── IM-0001-0074.dcm │ ├── IM-0001-0075.dcm │ ├── IM-0001-0076.dcm │ ├── IM-0001-0077.dcm │ ├── IM-0001-0078.dcm │ ├── IM-0001-0079.dcm │ ├── IM-0001-0080.dcm │ ├── IM-0001-0081.dcm │ ├── IM-0001-0082.dcm │ ├── IM-0001-0083.dcm │ ├── IM-0001-0084.dcm │ ├── IM-0001-0085.dcm │ ├── IM-0001-0086.dcm │ ├── IM-0001-0087.dcm │ ├── IM-0001-0088.dcm │ ├── IM-0001-0089.dcm │ ├── IM-0001-0090.dcm │ ├── IM-0001-0091.dcm │ ├── IM-0001-0092.dcm │ ├── IM-0001-0093.dcm │ ├── IM-0001-0094.dcm │ ├── IM-0001-0095.dcm │ ├── IM-0001-0096.dcm │ ├── IM-0001-0097.dcm │ ├── IM-0001-0098.dcm │ ├── IM-0001-0099.dcm │ ├── IM-0001-0100.dcm │ ├── IM-0001-0101.dcm │ ├── IM-0001-0102.dcm │ ├── IM-0001-0103.dcm │ ├── IM-0001-0104.dcm │ ├── IM-0001-0105.dcm │ ├── IM-0001-0106.dcm │ ├── IM-0001-0107.dcm │ ├── IM-0001-0108.dcm │ ├── IM-0001-0109.dcm │ ├── IM-0001-0110.dcm │ ├── IM-0001-0111.dcm │ ├── IM-0001-0112.dcm │ ├── IM-0001-0113.dcm │ ├── IM-0001-0114.dcm │ ├── IM-0001-0115.dcm │ ├── IM-0001-0116.dcm │ ├── IM-0001-0117.dcm │ ├── IM-0001-0118.dcm │ ├── IM-0001-0119.dcm │ ├── IM-0001-0120.dcm │ ├── IM-0001-0121.dcm │ ├── IM-0001-0122.dcm │ ├── IM-0001-0123.dcm │ ├── IM-0001-0124.dcm │ ├── IM-0001-0125.dcm │ ├── IM-0001-0126.dcm │ ├── IM-0001-0127.dcm │ ├── IM-0001-0128.dcm │ ├── IM-0001-0129.dcm │ ├── IM-0001-0130.dcm │ ├── IM-0001-0131.dcm │ ├── IM-0001-0132.dcm │ ├── IM-0001-0133.dcm │ ├── IM-0001-0134.dcm │ ├── IM-0001-0135.dcm │ ├── IM-0001-0136.dcm │ ├── IM-0001-0137.dcm │ ├── IM-0001-0138.dcm │ ├── IM-0001-0139.dcm │ ├── IM-0001-0140.dcm │ ├── IM-0001-0141.dcm │ ├── IM-0001-0142.dcm │ ├── IM-0001-0143.dcm │ ├── IM-0001-0144.dcm │ ├── IM-0001-0145.dcm │ ├── IM-0001-0146.dcm │ ├── IM-0001-0147.dcm │ ├── IM-0001-0148.dcm │ ├── IM-0001-0149.dcm │ ├── IM-0001-0150.dcm │ ├── IM-0001-0151.dcm │ ├── IM-0001-0152.dcm │ ├── IM-0001-0153.dcm │ ├── IM-0001-0154.dcm │ ├── IM-0001-0155.dcm │ ├── IM-0001-0156.dcm │ ├── IM-0001-0157.dcm │ ├── IM-0001-0158.dcm │ ├── IM-0001-0159.dcm │ ├── IM-0001-0160.dcm │ ├── IM-0001-0161.dcm │ ├── IM-0001-0162.dcm │ ├── IM-0001-0163.dcm │ ├── IM-0001-0164.dcm │ ├── IM-0001-0165.dcm │ ├── IM-0001-0166.dcm │ ├── IM-0001-0167.dcm │ ├── IM-0001-0168.dcm │ ├── IM-0001-0169.dcm │ ├── IM-0001-0170.dcm │ ├── IM-0001-0171.dcm │ ├── IM-0001-0172.dcm │ ├── IM-0001-0173.dcm │ ├── IM-0001-0174.dcm │ ├── IM-0001-0175.dcm │ ├── IM-0001-0176.dcm │ ├── IM-0001-0177.dcm │ ├── IM-0001-0178.dcm │ ├── IM-0001-0179.dcm │ ├── IM-0001-0180.dcm │ ├── IM-0001-0181.dcm │ ├── IM-0001-0182.dcm │ ├── IM-0001-0183.dcm │ ├── IM-0001-0184.dcm │ ├── IM-0001-0185.dcm │ ├── IM-0001-0186.dcm │ ├── IM-0001-0187.dcm │ ├── IM-0001-0188.dcm │ ├── IM-0001-0189.dcm │ ├── IM-0001-0190.dcm │ ├── IM-0001-0191.dcm │ ├── IM-0001-0192.dcm │ ├── IM-0001-0193.dcm │ ├── IM-0001-0194.dcm │ ├── IM-0001-0195.dcm │ ├── IM-0001-0196.dcm │ ├── IM-0001-0197.dcm │ ├── IM-0001-0198.dcm │ ├── IM-0001-0199.dcm │ ├── IM-0001-0200.dcm │ ├── IM-0001-0201.dcm │ ├── IM-0001-0202.dcm │ ├── IM-0001-0203.dcm │ ├── IM-0001-0204.dcm │ ├── IM-0001-0205.dcm │ ├── IM-0001-0206.dcm │ ├── IM-0001-0207.dcm │ ├── IM-0001-0208.dcm │ ├── IM-0001-0209.dcm │ ├── IM-0001-0210.dcm │ ├── IM-0001-0211.dcm │ ├── IM-0001-0212.dcm │ ├── IM-0001-0213.dcm │ ├── IM-0001-0214.dcm │ ├── IM-0001-0215.dcm │ ├── IM-0001-0216.dcm │ ├── IM-0001-0217.dcm │ ├── IM-0001-0218.dcm │ ├── IM-0001-0219.dcm │ ├── IM-0001-0220.dcm │ ├── IM-0001-0221.dcm │ ├── IM-0001-0222.dcm │ ├── IM-0001-0223.dcm │ ├── IM-0001-0224.dcm │ ├── IM-0001-0225.dcm │ ├── IM-0001-0226.dcm │ ├── IM-0001-0227.dcm │ ├── IM-0001-0228.dcm │ ├── IM-0001-0229.dcm │ ├── IM-0001-0230.dcm │ ├── IM-0001-0231.dcm │ ├── IM-0001-0232.dcm │ ├── IM-0001-0233.dcm │ ├── IM-0001-0234.dcm │ ├── IM-0001-0235.dcm │ ├── IM-0001-0236.dcm │ ├── IM-0001-0237.dcm │ ├── IM-0001-0238.dcm │ ├── IM-0001-0239.dcm │ ├── IM-0001-0240.dcm │ ├── IM-0001-0241.dcm │ ├── IM-0001-0242.dcm │ ├── IM-0001-0243.dcm │ ├── IM-0001-0244.dcm │ ├── IM-0001-0245.dcm │ ├── IM-0001-0246.dcm │ ├── IM-0001-0247.dcm │ ├── IM-0001-0248.dcm │ ├── IM-0001-0249.dcm │ ├── IM-0001-0250.dcm │ ├── IM-0001-0251.dcm │ ├── IM-0001-0252.dcm │ ├── IM-0001-0253.dcm │ ├── IM-0001-0254.dcm │ ├── IM-0001-0255.dcm │ ├── IM-0001-0256.dcm │ ├── IM-0001-0257.dcm │ ├── IM-0001-0258.dcm │ ├── IM-0001-0259.dcm │ ├── IM-0001-0260.dcm │ ├── IM-0001-0261.dcm │ ├── IM-0001-0262.dcm │ ├── IM-0001-0263.dcm │ ├── IM-0001-0264.dcm │ ├── IM-0001-0265.dcm │ ├── IM-0001-0266.dcm │ ├── IM-0001-0267.dcm │ ├── IM-0001-0268.dcm │ ├── IM-0001-0269.dcm │ ├── IM-0001-0270.dcm │ ├── IM-0001-0271.dcm │ ├── IM-0001-0272.dcm │ ├── IM-0001-0273.dcm │ ├── IM-0001-0274.dcm │ ├── IM-0001-0275.dcm │ ├── IM-0001-0276.dcm │ ├── IM-0001-0277.dcm │ ├── IM-0001-0278.dcm │ ├── IM-0001-0279.dcm │ ├── IM-0001-0280.dcm │ ├── IM-0001-0281.dcm │ ├── IM-0001-0282.dcm │ ├── IM-0001-0283.dcm │ ├── IM-0001-0284.dcm │ ├── IM-0001-0285.dcm │ ├── IM-0001-0286.dcm │ ├── IM-0001-0287.dcm │ ├── IM-0001-0288.dcm │ ├── IM-0001-0289.dcm │ ├── IM-0001-0290.dcm │ ├── IM-0001-0291.dcm │ ├── IM-0001-0292.dcm │ ├── IM-0001-0293.dcm │ ├── IM-0001-0294.dcm │ ├── IM-0001-0295.dcm │ ├── IM-0001-0296.dcm │ ├── IM-0001-0297.dcm │ ├── IM-0001-0298.dcm │ ├── IM-0001-0299.dcm │ ├── IM-0001-0300.dcm │ ├── IM-0001-0301.dcm │ ├── IM-0001-0302.dcm │ ├── IM-0001-0303.dcm │ ├── IM-0001-0304.dcm │ ├── IM-0001-0305.dcm │ ├── IM-0001-0306.dcm │ ├── IM-0001-0307.dcm │ ├── IM-0001-0308.dcm │ ├── IM-0001-0309.dcm │ ├── IM-0001-0310.dcm │ ├── IM-0001-0311.dcm │ ├── IM-0001-0312.dcm │ ├── IM-0001-0313.dcm │ ├── IM-0001-0314.dcm │ ├── IM-0001-0315.dcm │ ├── IM-0001-0316.dcm │ ├── IM-0001-0317.dcm │ ├── IM-0001-0318.dcm │ ├── IM-0001-0319.dcm │ ├── IM-0001-0320.dcm │ ├── IM-0001-0321.dcm │ ├── IM-0001-0322.dcm │ ├── IM-0001-0323.dcm │ ├── IM-0001-0324.dcm │ ├── IM-0001-0325.dcm │ ├── IM-0001-0326.dcm │ ├── IM-0001-0327.dcm │ ├── IM-0001-0328.dcm │ ├── IM-0001-0329.dcm │ ├── IM-0001-0330.dcm │ ├── IM-0001-0331.dcm │ ├── IM-0001-0332.dcm │ ├── IM-0001-0333.dcm │ ├── IM-0001-0334.dcm │ └── IM-0001-0335.dcm ├── TextureAnalysis ├── suvs.npy ├── __init__.py ├── Utils.py ├── GLHA.py ├── GLCM.py ├── NGTDM.py ├── GLSZM.py ├── NGTDM_3D.py └── 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/doc/Gallery.md: -------------------------------------------------------------------------------- 1 | # Gallery 2 | 3 | The following images were generated by [TextureAnalysis.ipynb](https://github.com/shinaji/texture_analysis/blob/master/doc/TextureAnalysis.ipynb) 4 | 5 | ## GLCM 6 | ![image](./imgs/glcm_example.png) 7 | 8 | ## GLSZM 9 | ![image](./imgs/glszm_example.png) 10 | 11 | ## NGTDM 12 | ![image](./imgs/ngtdm_example.png) 13 | 14 | ## GLHA 15 | ![image](./imgs/glha_example.png) -------------------------------------------------------------------------------- /TextureAnalysis/__init__.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | __init__.py 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/29 12 | 13 | """ 14 | __version__ = "0.1.0" 15 | from .GLCM_3D import GLCM_3D 16 | from .GLSZM_3D import GLSZM_3D 17 | from .NGTDM_3D import NGTDM_3D 18 | from .GLHA import GLHA 19 | from .NGTDM import NGTDM -------------------------------------------------------------------------------- /TextureAnalysis/Utils.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | Utills 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/29 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | 18 | 19 | def normalize(img, level_min=1, level_max=256, threshold=None): 20 | """ 21 | normalize the given image 22 | 23 | :param img: image array 24 | :param level_min: min intensity of normalized image 25 | :param level_max: max intensity of normalized image 26 | :param threshold: threshold of the minimal value 27 | 28 | :return: normalized image array, slope, intercept 29 | """ 30 | 31 | tmp_img = np.array(img) 32 | if threshold is None: 33 | threshold = tmp_img.min() 34 | tmp_img[tmp_img 37 | 38 | http://www.dgp.toronto.edu/~mac/e-stuff/point_in_polygon.py 39 | -------------------------------------------------------------------------------- /Utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | """ 3 | Utils.py 4 | 5 | Copyright (c) 2017 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2017/01/03 12 | 13 | The major design of routine for performing the "point in polygon" inclusion 14 | was performed by softSurfer. 15 | The softSurfer's code was translated to Python by Maciej Kalisiak. 16 | Here is the original copyright notice. 17 | 18 | Copyright 2001, softSurfer (www.softsurfer.com) 19 | This code may be freely used and modified for any purpose 20 | providing that this copyright notice is included with it. 21 | SoftSurfer makes no warranty for this code, and cannot be held 22 | liable for any real or imagined damage resulting from its use. 23 | Users of this code must verify correctness for their application. 24 | 25 | translated to Python by Maciej Kalisiak 26 | 27 | http://www.dgp.toronto.edu/~mac/e-stuff/point_in_polygon.py 28 | 29 | """ 30 | import numpy as np 31 | 32 | 33 | def is_inside(z0, z1, pet_img): 34 | """ 35 | check if z0 is within z1 polygon 36 | :param z0: point 37 | :param z1: vertex positions of roi polygon 38 | :param pet_img: pet image 39 | :return: 40 | """ 41 | tmp = np.ones_like(pet_img) * -1 42 | for i in range(len(z0)): 43 | if __get_wn(z0[i], z1): 44 | tmp[z0[i][1], z0[i][0]] = pet_img[z0[i][1], z0[i][0]] 45 | return tmp 46 | 47 | 48 | def __is_left(P0, P1, P2): 49 | """ 50 | check if P2 is left of the vector P0->P1 51 | :param P0: vertex position of polygon 52 | :param P1: vertex position of polygon next to p1 53 | :param P2: target position for this check process 54 | """ 55 | return (P1[0] - P0[0]) * (P2[1] - P0[1]) - (P2[0] - P0[0]) * (P1[1] - P0[1]) 56 | 57 | 58 | def __get_wn(z0, z1): 59 | """ 60 | retrun wn value of the Winding Number Algorithm 61 | :param z0: target position for this check process 62 | :param z1: vertex positions of polygon 63 | :return: wn 64 | """ 65 | 66 | wn = 0 67 | for i in range(z1.shape[0] - 1): 68 | 69 | v1 = z1[i + 1] - z1[i] 70 | v2 = z1[i + 1] - z0 71 | lv1 = np.linalg.norm(v1) 72 | lv2 = np.linalg.norm(v2) 73 | if lv2 == 0: 74 | return 1 75 | if (np.abs(np.dot(v1 / lv1, v2 / lv2) - 1) <= 1e-5) and ( 76 | lv1 >= lv2) and (lv1 > np.linalg.norm(v1 + v2)): 77 | return 1 78 | 79 | if z1[i, 1] <= z0[1]: # start y <= P[1] 80 | if z1[i + 1, 1] > z0[1]: # an upward crossing 81 | if __is_left(z1[i], z1[i + 1], z0) > 0: # P left of edge 82 | wn += 1 # have a valid up intersect 83 | else: # start y > P[1] (no test needed) 84 | if z1[i + 1, 1] <= z0[1]: # a downward crossing 85 | if __is_left(z1[i], z1[i + 1], z0) < 0: # P right of edge 86 | wn -= 1 # have a valid down intersect 87 | return wn 88 | -------------------------------------------------------------------------------- /TextureAnalysis/GLHA.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | GLHA 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/29 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | 19 | class GLHA: 20 | """ 21 | Gray Level Histogram Analysis 22 | """ 23 | def __init__(self, img, level_min=1, level_max=256, threshold=None): 24 | """ 25 | initialize 26 | 27 | :param img: normalized image 28 | :param level_min: min intensity of normalized image 29 | :param level_max: max intensity of normalized image 30 | :param threshold: threshold of the minimal value 31 | """ 32 | 33 | self.img, self.slope, self.intercept = \ 34 | normalize(img, level_min, level_max, threshold) 35 | self.n_level = (level_max - level_min) + 1 36 | self.level_min = level_min 37 | self.level_max = level_max 38 | 39 | hist, bin_edges = np.histogram(self.img.flatten(), 40 | bins=self.n_level, 41 | range=[level_min, level_max], 42 | density=False) 43 | self.hist = np.array(hist) 44 | hist, bin_edges = np.histogram(self.img.flatten(), 45 | bins=self.n_level, 46 | range=[level_min, level_max], 47 | density=True) 48 | self.bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 49 | 50 | self.p_glha = np.array(hist) 51 | self.features = self._calc_features() 52 | 53 | def _calc_features(self): 54 | """ 55 | calculate feature values 56 | 57 | :return: feature values 58 | """ 59 | 60 | features = {} 61 | x = np.arange(self.level_min, self.level_max+1) 62 | features['mean'] = (self.p_glha*x).sum() 63 | features['sd'] = np.sqrt((self.p_glha*(x-features['mean'])**2).sum()) 64 | features['skewness'] = \ 65 | (self.p_glha*(x-features['mean'])**3).sum() / features['sd']**3 66 | features['kurtosis'] = \ 67 | (self.p_glha*(x-features['mean'])**4).sum() / features['sd']**4 68 | 69 | return features 70 | 71 | def print_features(self, show_figure=False): 72 | """ 73 | print features 74 | 75 | :param show_figure: if True, show figure 76 | """ 77 | 78 | print("----GLHA-----") 79 | feature_labels = [] 80 | feature_values = [] 81 | for key in sorted(self.features.keys()): 82 | print("{}: {}".format(key, self.features[key])) 83 | feature_labels.append(key) 84 | feature_values.append(self.features[key]) 85 | 86 | if show_figure: 87 | plt.plot(self.bin_centers, self.p_glha, 'o-b', label='Dencity') 88 | plt.plot([self.features['mean'], self.features['mean']], 89 | [0, self.p_glha.max()*1.2], '-r', label='mean') 90 | plt.plot([self.features['mean']-self.features['sd'], 91 | self.features['mean']-self.features['sd']], 92 | [0, self.p_glha.max()*1.2], '-.r', label='sd lower') 93 | plt.plot([self.features['mean']+self.features['sd'], 94 | self.features['mean']+self.features['sd']], 95 | [0, self.p_glha.max()*1.2], '-.r', label='sd upper') 96 | plt.legend(loc=0, numpoints=1) 97 | plt.ylim(0, self.p_glha.max()*1.2) 98 | plt.show() 99 | 100 | return feature_labels, feature_values 101 | 102 | if __name__ == '__main__': 103 | pass -------------------------------------------------------------------------------- /TextureAnalysis/GLCM.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | GLCM 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/29 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | 19 | 20 | class GLCM: 21 | """ 22 | Gray-Level Co-occurrence Matrix 23 | """ 24 | def __init__(self, img, theta=[0, 1], level_min=1, level_max=256, threshold=None): 25 | """ 26 | initialize 27 | 28 | :param img: normalized image 29 | :param theta: definition of neighbor 30 | :param level_min: min intensity of normalized image 31 | :param level_max: max intensity of normalized image 32 | :param threshold: threshold of the minimal value 33 | """ 34 | 35 | assert len(img.shape) == 2, 'image must be 2D' 36 | self.img, self.slope, self.intercept = \ 37 | normalize(img, level_min, level_max, threshold) 38 | self.n_level = (level_max - level_min) + 1 39 | self.level_min = level_min 40 | self.level_max = level_max 41 | self.theta = theta 42 | self.matrix = self._construct_matrix() 43 | self.features = self._calc_features() 44 | 45 | def _calc_features(self): 46 | """ 47 | calculate feature values 48 | 49 | :return: feature values 50 | """ 51 | 52 | I, J = np.ogrid[self.level_min:self.level_max+1, 53 | self.level_min:self.level_max+1] 54 | mat = np.array(self.matrix) 55 | features = {} 56 | features['uniformity'] = (mat**2).sum() 57 | features['entropy'] = -(mat[mat>0]*np.log(mat[mat>0])).sum() 58 | features['dissimilarity'] = (mat*np.abs(I-J)).sum() 59 | features['contrast'] = (mat*((I-J)**2)).sum() 60 | features['homogeneity'] = (mat/(1+np.abs(I-J))).sum() 61 | features['inverse difference moment'] = (mat/(1+(I-J)**2)).sum() 62 | features['maximum probability'] = mat.max()*100 63 | return features 64 | 65 | 66 | def _construct_matrix(self): 67 | """ 68 | construct GLC-Matrix 69 | 70 | :return: GLC-Matrix 71 | """ 72 | 73 | mat = np.zeros((self.n_level, self.n_level)).astype(np.float) 74 | unique = np.unique(self.img) 75 | width = self.img.shape[1] 76 | height = self.img.shape[0] 77 | print(self.img.max()) 78 | print(self.img.min()) 79 | for uni in unique: 80 | if uni < self.level_min: 81 | continue 82 | indices = np.argwhere(self.img == uni) 83 | for idx in indices: 84 | pos = np.array(idx + self.theta, dtype=np.int32) 85 | if 0 <= pos[0] < height and 0 <= pos[1] < width: 86 | neighbor_value = self.img[pos[0], pos[1]] 87 | if neighbor_value >= self.level_min: 88 | mat[self.img[idx[0], idx[1]]-self.level_min, 89 | self.img[pos[0], pos[1]]-self.level_min] += 1 90 | pos = idx + (self.theta*-1) 91 | if 0 <= pos[0] < height and 0 <= pos[1] < width: 92 | neighbor_value = self.img[pos[0], pos[1]] 93 | if neighbor_value >= self.level_min: 94 | mat[self.img[idx[0], idx[1]]-self.level_min, 95 | self.img[pos[0], pos[1]]-self.level_min] += 1 96 | return mat 97 | 98 | 99 | if __name__ == '__main__': 100 | 101 | suvs = np.load("suvs.npy") 102 | mats = [] 103 | for i in range(suvs.shape[0]): 104 | glcm = GLCM(suvs[i], theta=np.array([0, 1]), 105 | level_min=1, level_max=32, 106 | threshold=suvs.max()*0.5) 107 | mats.append(glcm.matrix) 108 | mats = np.array(mats) 109 | print(mats) 110 | print(mats.shape) -------------------------------------------------------------------------------- /TextureAnalysis/NGTDM.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | NGTDM 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/31 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | from scipy import signal 19 | 20 | class NGTDM: 21 | """ 22 | Neighbourhood Gray-Tone-Difference Matrix 23 | """ 24 | def __init__(self, img, d=1, level_min=1, level_max=256, threshold=None): 25 | """ 26 | initialize 27 | 28 | :param img: normalized image 29 | :param d: distance 30 | :param level_min: min intensity of normalized image 31 | :param level_max: max intensity of normalized image 32 | :param threshold: threshold of the minimal value 33 | """ 34 | 35 | assert len(img.shape) == 2, 'image must be 2D' 36 | self.img, self.slope, self.intercept = \ 37 | normalize(img, level_min, level_max, threshold) 38 | self.n_level = (level_max - level_min) + 1 39 | self.level_min = level_min 40 | self.level_max = level_max 41 | self.d = d 42 | self.s, self.p, self.ng, self.n2 = self._construct_matrix() 43 | self.features = self._calc_features() 44 | 45 | def _calc_features(self): 46 | """ 47 | calculate feature values 48 | 49 | :return: feature values 50 | """ 51 | features = {} 52 | I, J = np.ogrid[self.level_min:self.level_max+1, 53 | self.level_min:self.level_max+1] 54 | pi = np.hstack((self.p[:, np.newaxis],)*len(self.p)) 55 | pj = np.vstack((self.p[np.newaxis, :],)*len(self.p)) 56 | # ipi = pi * np.hstack((np.arange(len(self.p))[:, np.newaxis],)*len(self.p)) 57 | # jpj = pj * np.vstack((np.arange(len(self.p))[np.newaxis, :],)*len(self.p)) 58 | # ipi = self.p * np.arange(self.level_min, self.level_max + 1)[:,np.newaxis] 59 | # jpj = self.p * np.arange(self.level_min, self.level_max + 1)[np.newaxis,:] 60 | ipi = np.hstack( 61 | ((self.p*np.arange(1, len(self.p)+1))[:, np.newaxis],) * len(self.p)) 62 | jpj = np.vstack( 63 | ((self.p*np.arange(1, len(self.p)+1))[np.newaxis, :],) * len(self.p)) 64 | pisi = pi * np.hstack((self.s[:, np.newaxis],)*len(self.p)) 65 | pjsj = pj * np.vstack((self.s[np.newaxis, :],)*len(self.p)) 66 | fcos = 1.0 / (1e-6 + (self.p*self.s).sum()) 67 | fcon = 1.0 / (self.ng*(self.ng-1)) * (pi*pj*(I-J)**2).sum() * (self.s.sum()/self.n2) 68 | mask1 = np.logical_and(pi > 0, pj > 0) 69 | mask2 = self.p > 0 70 | if (np.abs(ipi[mask1] - jpj[mask1])).sum() == 0: 71 | fbus = np.inf 72 | else: 73 | fbus = (self.p*self.s)[mask2].sum() / (np.abs(ipi[mask1] - jpj[mask1])).sum() 74 | fcom = (np.abs(I-J)[mask1] / (self.n2*(pi+pj)[mask1]) * (pisi + pjsj)[mask1]).sum() 75 | fstr = ((pi + pj) * (I-J)**2).sum() / (1e-6+self.s.sum()) 76 | features['coarseness'] = fcos 77 | features['contrast'] = fcon 78 | features['busyness'] = fbus 79 | features['complexity'] = fcom 80 | features['strength'] = fstr 81 | return features 82 | 83 | def _construct_matrix(self): 84 | """ 85 | construct NGTD-Matrix 86 | 87 | :return: NGTD-Matrix 88 | """ 89 | 90 | assert self.d > 0, 'd must be grater than 1' 91 | assert self.level_min > 0, 'lower level must be greater than 0' 92 | # w = (2 * self.d + 1)**2 93 | kernel = np.ones((2*self.d+1, 2*self.d+1)) 94 | kernel[self.d, self.d] = 0 95 | h, w = self.img.shape 96 | A = signal.convolve2d(self.img, kernel, mode='valid') 97 | A *= (1/((2 * self.d + 1)**2-1)) 98 | s = np.zeros(self.n_level) 99 | p = np.zeros_like(s) 100 | crop_img = np.array(self.img[self.d:h-self.d, self.d:w-self.d]) 101 | for i in range(self.level_min, self.level_max+1): 102 | indices = np.argwhere(crop_img == i) 103 | s[i-self.level_min] = np.abs(i - A[indices[:, 0], indices[:, 1]]).sum() 104 | p[i-self.level_min] = float(len(indices)) / np.prod(crop_img.shape) 105 | ng = np.sum(np.unique(crop_img)>=0) 106 | n2 = np.prod(crop_img.shape) 107 | return s, p, ng, n2 108 | 109 | 110 | if __name__ == '__main__': 111 | pass -------------------------------------------------------------------------------- /TextureAnalysis/GLSZM.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | GLSZM 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/31 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | from scipy.ndimage import measurements 19 | 20 | 21 | class GLSZM: 22 | """ 23 | Gray Level Size Zone Matrix 24 | """ 25 | def __init__(self, img, level_min=1, level_max=256, threshold=None): 26 | """ 27 | initialize 28 | 29 | :param img: normalized image 30 | :param level_min: min intensity of normalized image 31 | :param level_max: max intensity of normalized image 32 | :param threshold: threshold of the minimal value 33 | """ 34 | 35 | assert len(img.shape) == 2, 'image must be 2D' 36 | self.img, self.slope, self.intercept = \ 37 | normalize(img, level_min, level_max, threshold) 38 | self.n_level = (level_max - level_min) + 1 39 | self.level_min = level_min 40 | self.level_max = level_max 41 | self.matrix, self.zone_sizes = self._construct_matrix() 42 | self.features = self._calc_features() 43 | 44 | def _calc_features(self): 45 | """ 46 | calculate feature values 47 | 48 | :return: feature values 49 | """ 50 | 51 | features ={} 52 | mat = self.matrix 53 | zone_sizes = self.zone_sizes 54 | 55 | omega = mat.flatten().sum() 56 | min_size = zone_sizes.min() 57 | max_size = zone_sizes.max() 58 | j = np.array(range(min_size, max_size+1))[np.newaxis, :] 59 | j = np.vstack((j,)*mat.shape[0]) 60 | i = np.array(range(self.level_min, self.level_max+1))[:, np.newaxis] 61 | i = np.hstack((i,)*mat.shape[1]) 62 | small_area_emp = (mat / (j**2)).sum() / omega 63 | large_area_emp = (mat * (j**2)).sum() / omega 64 | low_intensity_emp = (mat / (i**2)).sum() / omega 65 | high_intensity_emp = (mat * (i**2)).sum() / omega 66 | intensity_variability = ((mat / (i**2)).sum(axis=1) ** 2).sum() / omega 67 | size_zone_variability = ((mat / (j**2)).sum(axis=0) ** 2).sum() / omega #? 68 | zone_percentage = omega / (mat * (j**2)).sum() 69 | low_intensity_small_area_emp = (mat / (i**2) / (j**2)).sum() / omega 70 | high_intensity_small_area_emp = (mat * (i**2) * (j**2)).sum() / omega 71 | low_intensity_large_area_emp = (mat * (j**2) / (i**2)).sum() / omega 72 | high_intensity_large_area_emp = (mat * (i**2) / (j**2)).sum() / omega 73 | 74 | features['small_area_emp'] = small_area_emp 75 | features['large_area_emp'] = large_area_emp 76 | features['low_intensity_emp'] = low_intensity_emp 77 | features['high_intensity_emp'] = high_intensity_emp 78 | features['intensity_variability'] = intensity_variability 79 | features['size_zone_variability'] = size_zone_variability 80 | features['zone_percentage'] = zone_percentage 81 | features['low_intensity_small_area_emp'] = low_intensity_small_area_emp 82 | features['high_intensity_small_area_emp'] = high_intensity_small_area_emp 83 | features['low_intensity_large_area_emp'] = low_intensity_large_area_emp 84 | features['high_intensity_large_area_emp'] = high_intensity_large_area_emp 85 | 86 | 87 | def _construct_matrix(self): 88 | """ 89 | construct GLSZ-Matrix 90 | 91 | :return: GLSZ-Matrix 92 | """ 93 | s = [[1, 1, 1], 94 | [1, 1, 1], 95 | [1, 1, 1]] 96 | elements = [] 97 | for i in range(self.level_min, self.level_max+1): 98 | assert i >= 0, 'level mast be positivee value or 0.' 99 | tmp_img = np.array(self.img) 100 | tmp_img = (tmp_img == i) 101 | labeled_array, num_features = measurements.label(tmp_img, 102 | structure=s) 103 | for label in range(1, num_features+1): 104 | size = (labeled_array.flatten() == label).sum() 105 | elements.append([i, size]) 106 | 107 | elements = np.array(elements) 108 | min_element_size = elements[:, 1].min() 109 | rows = (self.level_max - self.level_min) + 1 110 | cols = elements[:, 1].max() - min_element_size + 1 111 | mat = np.zeros((rows, cols), dtype=np.float) 112 | zone_sizes = np.unique(elements[:, 1]) 113 | for element in elements: 114 | mat[element[0], element[1]-min_element_size] += 1 115 | 116 | return mat, zone_sizes 117 | 118 | 119 | if __name__ == '__main__': 120 | pass -------------------------------------------------------------------------------- /TextureAnalysis/NGTDM_3D.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | NGTDM_3D 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/31 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | from scipy.ndimage.filters import convolve 19 | 20 | class NGTDM_3D: 21 | """ 22 | Neighbourhood Gray-Tone-Difference Matrix 23 | """ 24 | def __init__(self, img, d=1, level_min=1, level_max=127, threshold=None): 25 | """ 26 | initialize 27 | 28 | :param img: 3D image 29 | :param d: distance 30 | :param level_min: min intensity of normalized image 31 | :param level_max: max intensity of normalized image 32 | :param threshold: threshold of the minimal value 33 | """ 34 | 35 | assert len(img.shape) == 3, 'image must be 3D' 36 | 37 | self.img, self.slope, self.intercept = \ 38 | normalize(img, level_min, level_max, threshold) 39 | self.img[self.img 0, 'lower level must be greater than 0' 44 | self.d = d 45 | assert self.d > 0, 'd must be grater than 0' 46 | self.s, self.p, self.ng, self.n2 = self._construct_matrix() 47 | self.features = self._calc_features() 48 | 49 | def _calc_features(self): 50 | """ 51 | calculate feature values 52 | 53 | :return: feature values 54 | """ 55 | features = {} 56 | I, J = np.ogrid[self.level_min:self.level_max+1, 57 | self.level_min:self.level_max+1] 58 | pi = np.hstack((self.p[:, np.newaxis],)*len(self.p)) 59 | pj = np.vstack((self.p[np.newaxis, :],)*len(self.p)) 60 | # ipi = self.p*np.arange(self.level_min, self.level_max+1)[:, np.newaxis] 61 | # jpj = self.p*np.arange(self.level_min, self.level_max+1)[np.newaxis, :] 62 | # ipi = pi * np.hstack((np.arange(self.level_min, self.level_max+1)[:, np.newaxis],)*len(self.p)) 63 | # jpj = pj * np.vstack((np.arange(self.level_min, self.level_max+1)[np.newaxis, :],)*len(self.p)) 64 | ipi = np.hstack( 65 | ((self.p * np.arange(1, len(self.p) + 1))[:, np.newaxis],) * len( 66 | self.p)) 67 | jpj = np.vstack( 68 | ((self.p * np.arange(1, len(self.p) + 1))[np.newaxis, :],) * len( 69 | self.p)) 70 | pisi = pi * np.hstack((self.s[:, np.newaxis],)*len(self.p)) 71 | pjsj = pj * np.vstack((self.s[np.newaxis, :],)*len(self.p)) 72 | fcos = 1.0 / (1e-6 + (self.p*self.s).sum()) 73 | fcon = 1.0 / (self.ng*(self.ng-1)) * (pi*pj*(I-J)**2).sum() * (self.s.sum()/self.n2) 74 | mask1 = np.logical_and(pi > 0, pj > 0) 75 | mask2 = self.p > 0 76 | if (np.abs(ipi[mask1] - jpj[mask1])).sum() == 0: 77 | fbus = np.inf 78 | else: 79 | fbus = (self.p * self.s)[mask2].sum() / ( 80 | np.abs(ipi[mask1] - jpj[mask1])).sum() 81 | fcom = (np.abs(I - J)[mask1] / (self.n2 * (pi + pj)[mask1]) * 82 | (pisi + pjsj)[mask1]).sum() 83 | fstr = ((pi + pj) * (I - J) ** 2).sum() / (1e-6 + self.s.sum()) 84 | features['coarseness'] = fcos 85 | features['contrast'] = fcon 86 | features['busyness'] = fbus 87 | features['complexity'] = fcom 88 | features['strength'] = fstr 89 | return features 90 | 91 | def print_features(self, show_figure=False): 92 | """ 93 | print features 94 | 95 | :param show_figure: if True, show figure 96 | """ 97 | 98 | print("----NGTDM 3D-----") 99 | feature_labels = [] 100 | feature_values = [] 101 | for key in sorted(self.features.keys()): 102 | print("{}: {}".format(key, self.features[key])) 103 | feature_labels.append(key) 104 | feature_values.append(self.features[key]) 105 | 106 | if show_figure: 107 | plt.plot(range(self.level_min, self.level_max+1), 108 | self.p, '-ob') 109 | plt.show() 110 | 111 | return feature_labels, feature_values 112 | 113 | def _construct_matrix(self): 114 | """ 115 | construct NGTD-Matrix 116 | 117 | :return: NGTD-Matrix 118 | """ 119 | 120 | kernel = np.ones((2*self.d+1, 2*self.d+1, 2*self.d+1)) 121 | kernel[self.d, self.d, self.d] = 0 122 | d, h, w = self.img.shape 123 | A = convolve(self.img.astype(float), kernel, mode='constant') 124 | A *= (1./((2 * self.d + 1)**3-1)) 125 | s = np.zeros(self.n_level) 126 | p = np.zeros_like(s) 127 | crop_img = np.array(self.img[self.d:d-self.d, 128 | self.d:h-self.d, 129 | self.d:w-self.d]) 130 | for i in range(self.level_min, self.level_max+1): 131 | indices = np.argwhere(crop_img == i) + np.array([self.d, 132 | self.d, 133 | self.d]) 134 | s[i-self.level_min] = \ 135 | np.abs(i - A[indices[:, 0], 136 | indices[:, 1], 137 | indices[:, 2]]).sum() 138 | p[i-self.level_min] = float(len(indices)) / np.prod(crop_img.shape) 139 | 140 | ng = np.sum(np.unique(crop_img)>0) 141 | n2 = np.prod(crop_img.shape) 142 | return s, p, ng, n2 143 | 144 | 145 | if __name__ == '__main__': 146 | pass -------------------------------------------------------------------------------- /TextureAnalysis/GLCM_3D.py: -------------------------------------------------------------------------------- 1 | #-*- coding:utf-8 -*- 2 | """ 3 | GLCM_3D 4 | 5 | Copyright (c) 2016 Tetsuya Shinaji 6 | 7 | This software is released under the MIT License. 8 | 9 | http://opensource.org/licenses/mit-license.php 10 | 11 | Date: 2016/01/29 12 | 13 | """ 14 | 15 | import numpy as np 16 | from matplotlib import pyplot as plt 17 | from TextureAnalysis.Utils import normalize 18 | 19 | 20 | class GLCM_3D: 21 | """ 22 | c 23 | """ 24 | def __init__(self, img, d=1, level_min=1, level_max=256, threshold=None): 25 | """ 26 | initialize 27 | 28 | :param img: normalized image 29 | :param d: distance from center 30 | :param level_min: min intensity of normalized image 31 | :param level_max: max intensity of normalized image 32 | :param threshold: threshold of the minimal value 33 | """ 34 | 35 | assert len(img.shape) == 3, 'image must be 3D' 36 | self.img, self.slope, self.intercept = \ 37 | normalize(img, level_min, level_max, threshold) 38 | self.n_level = (level_max - level_min) + 1 39 | self.level_min = level_min 40 | self.level_max = level_max 41 | self.d = d 42 | if self.d > 1: 43 | raise Exception("d>1 has not been implemented yet....") 44 | self.matrix_non_norm = self._construct_matrix() 45 | self.matrix = self.matrix_non_norm/self.matrix_non_norm.sum() 46 | self.features = self._calc_features() 47 | 48 | def _calc_features(self): 49 | """ 50 | calculate feature values 51 | 52 | :return: feature values 53 | """ 54 | 55 | I, J = np.ogrid[self.level_min:self.level_max+1, 56 | self.level_min:self.level_max+1] 57 | mat = np.array(self.matrix) 58 | features = {} 59 | features['uniformity'] = (mat**2).sum() 60 | features['entropy'] = -(mat[mat>0]*np.log(mat[mat>0])).sum() 61 | features['dissimilarity'] = (mat*np.abs(I-J)).sum() 62 | features['contrast'] = (mat*((I-J)**2)).sum() 63 | features['homogeneity'] = (mat/(1+np.abs(I-J))).sum() 64 | features['inverse difference moment'] = (mat/(1+(I-J)**2)).sum() 65 | features['maximum probability'] = mat.max()*100 66 | return features 67 | 68 | def print_features(self, show_figure=False): 69 | """ 70 | print features 71 | 72 | :param show_figure: if True, show figure 73 | """ 74 | 75 | print("----GLCM 3D-----") 76 | feature_labels = [] 77 | feature_values = [] 78 | for key in sorted(self.features.keys()): 79 | print("{}: {}".format(key, self.features[key])) 80 | feature_labels.append(key) 81 | feature_values.append(self.features[key]) 82 | 83 | if show_figure: 84 | plt.imshow(self.matrix, 85 | origin='lower', 86 | interpolation='none') 87 | plt.show() 88 | 89 | return feature_labels, feature_values 90 | 91 | 92 | def _construct_matrix(self): 93 | """ 94 | construct GLC-Matrix 95 | 96 | :return: GLC-Matrix 97 | """ 98 | 99 | mat = np.zeros((self.n_level, self.n_level)).astype(np.float) 100 | unique = np.unique(self.img) 101 | depth = self.img.shape[0] 102 | height = self.img.shape[1] 103 | width = self.img.shape[2] 104 | for uni in unique: 105 | if uni < self.level_min: 106 | continue 107 | indices = np.argwhere(self.img == uni) 108 | for idx in indices: 109 | positions = [ 110 | idx + np.array([-1, -1, -1]), 111 | idx + np.array([-1, -1, 0]), 112 | idx + np.array([-1, -1, 1]), 113 | idx + np.array([-1, 0, -1]), 114 | idx + np.array([-1, 0, 0]), 115 | idx + np.array([-1, 0, 1]), 116 | idx + np.array([-1, 1, -1]), 117 | idx + np.array([-1, 1, 0]), 118 | idx + np.array([-1, 1, 1]), 119 | idx + np.array([ 0, -1, -1]), 120 | idx + np.array([ 0, -1, 0]), 121 | idx + np.array([ 0, -1, 1]), 122 | idx + np.array([ 0, 0, -1]), 123 | idx + np.array([ 0, 0, 1]), 124 | idx + np.array([ 0, 1, -1]), 125 | idx + np.array([ 0, 1, 0]), 126 | idx + np.array([ 0, 1, 1]), 127 | idx + np.array([ 1, -1, -1]), 128 | idx + np.array([ 1, -1, 0]), 129 | idx + np.array([ 1, -1, 1]), 130 | idx + np.array([ 1, 0, -1]), 131 | idx + np.array([ 1, 0, 0]), 132 | idx + np.array([ 1, 0, 1]), 133 | idx + np.array([ 1, 1, -1]), 134 | idx + np.array([ 1, 1, 0]), 135 | idx + np.array([ 1, 1, 1]), 136 | ] 137 | for pos in positions: 138 | if 0 <= pos[0] < depth and 0 <= pos[1] < height and 0 <= pos[2] < width: 139 | neighbor_value = self.img[pos[0], pos[1], pos[2]] 140 | if neighbor_value >= self.level_min: 141 | mat[self.img[idx[0], idx[1], idx[2]]-self.level_min, 142 | self.img[pos[0], pos[1], pos[2]]-self.level_min] += 1 143 | return mat 144 | 145 | 146 | if __name__ == '__main__': 147 | 148 | suvs = np.load("suvs.npy") 149 | mats = [] 150 | for i in range(suvs.shape[0]): 151 | glcm = GLCM_3D(suvs, theta=np.array([0, 1]), 152 | level_min=1, level_max=32, 153 | threshold=suvs.max()*0.5) 154 | mats.append(glcm.matrix) 155 | mats = np.array(mats) 156 | print(mats) 157 | print(mats.shape) --------------------------------------------------------------------------------