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| # This is an example of settings that can be used as a starting point for analyzing CT data. This is only intended as a | |
| # starting point and is not likely to be the optimal settings for your dataset. Some points in determining better values | |
| # are added as comments where appropriate | |
| # When adapting and using these settings for an analysis, be sure to add the PyRadiomics version used to allow you to | |
| # easily recreate your extraction at a later timepoint: | |
| # ############################# Extracted using PyRadiomics version: <version> ###################################### | |
| imageType: | |
| Original: {} | |
| #LoG: | |
| # sigma: [1.0, 2.0] #, 3.0, 4.0, 5.0] # If you include sigma values >5, remember to also increase the padDistance. | |
| Wavelet: {} | |
| featureClass: | |
| # redundant Compactness 1, Compactness 2 an Spherical Disproportion features are disabled by default, they can be | |
| # enabled by specifying individual feature names (as is done for glcm) and including them in the list. | |
| #shape: | |
| firstorder: | |
| glcm: # Disable SumAverage by specifying all other GLCM features available | |
| - "Autocorrelation" | |
| - "JointAverage" | |
| - "ClusterProminence" | |
| - "ClusterShade" | |
| - "ClusterTendency" | |
| - "Contrast" | |
| - "Correlation" | |
| - "DifferenceAverage" | |
| - "DifferenceEntropy" | |
| - "DifferenceVariance" | |
| - "JointEnergy" | |
| - "JointEntropy" | |
| - "Imc1" | |
| - "Imc2" | |
| - "Idm" | |
| - "Idmn" | |
| - "Id" | |
| - "Idn" | |
| - "InverseVariance" | |
| - "MaximumProbability" | |
| - "SumEntropy" | |
| - "SumSquares" | |
| #glrlm: | |
| #glszm: | |
| gldm: | |
| setting: | |
| # Normalization: | |
| # most likely not needed, CT gray values reflect absolute world values (HU) and should be comparable between scanners. | |
| # If analyzing using different scanners / vendors, check if the extracted features are correlated to the scanner used. | |
| # If so, consider enabling normalization by uncommenting settings below: | |
| #normalize: true | |
| #normalizeScale: 500 # This allows you to use more or less the same bin width. | |
| # Resampling: | |
| # Usual spacing for CT is often close to 1 or 2 mm, if very large slice thickness is used, | |
| # increase the resampled spacing. | |
| # On a side note: increasing the resampled spacing forces PyRadiomics to look at more coarse textures, which may or | |
| # may not increase accuracy and stability of your extracted features. | |
| #interpolator: "sitkBSpline" | |
| #resampledPixelSpacing: [1, 1] | |
| # padDistance: 10 # Extra padding for large sigma valued LoG filtered images | |
| # Mask validation: | |
| # correctMask and geometryTolerance are not needed, as both image and mask are resampled, if you expect very small | |
| # masks, consider to enable a size constraint by uncommenting settings below: | |
| #minimumROIDimensions: 2 | |
| #minimumROISize: 50 | |
| # Image discretization: | |
| # The ideal number of bins is somewhere in the order of 16-128 bins. A possible way to define a good binwidt is to | |
| # extract firstorder:Range from the dataset to analyze, and choose a binwidth so, that range/binwidth remains approximately | |
| # in this range of bins. | |
| binWidth: 25 | |
| # first order specific settings: | |
| #voxelArrayShift: 1000 # Minimum value in HU is -1000, shift +1000 to prevent negative values from being squared. | |
| # Misc: | |
| # default label value. Labels can also be defined in the call to featureextractor.execute, as a commandline argument, | |
| # or in a column "Label" in the input csv (batchprocessing) | |
| label: 1 | |