# 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:

    #start_level: 0

    #level: 3

    wavelet: 'coif1'

  #Square: {}

  #SquareRoot: {}

  #Logarithm: {}

  #Exponential: {}

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:

  ngtdm:

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, 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