njjz_all_features.yaml

# 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: [0.5, 1.0, 2.0] 
  Wavelet: {}
  LBP3D:
    binWidth: 1.0
  Square: {}
  #SquareRoot: {}
  Logarithm: {}
  #Exponential: {}
  Gradient: {}
  #LBP2D: {}

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:
  #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