tumor.yaml

#######http://www.mpip.sdnu.edu.cn/##############################################################
#######https://pyradiomics.readthedocs.io/en/latest/index.html###################################
#######MRI-based Radiomics Extraction using PyRadiomics #########################################

imageType:
  Original: {}
  LoG:
    # If the in-plane spacing is large (> 2mm), consider removing sigma value 1.
    sigma: [1.0, 3.0, 5.0]
  Wavelet: {}
    #start_level: 0
    #level: 3
    #wavelet: 'coif1' # There are many other wavelet algorithms.
  #Square: {}
  #SquareRoot: {}
  #Logarithm: {}
  #Exponential: {}
  #Gradient：{}
  #LocalBinaryPattern2D：{}
  #LocalBinaryPattern3D：{}

featureClass:
  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:
  # MR signal is usually relative, with large differences between scanners and vendors. By normalizing the image before
  # feature calculation, this confounding effect may be reduced. However, if only one specific scanner is used, or the
  # images reflect some absolute world value (e.g. ADC maps, T2maps (NOT T2 weighted)), consider disabling the
  # normalization.
  normalize: true
  normalizeScale: 100  # This allows you to use more or less the same bin width.

  # Resampling:
  # Not enabled in this example. However, because texture calculation assumes isotropic spacing, a forced 2D extraction
  # is used, therefore only requiring the voxels to be isotropic in-plane. Enable pre-cropping to reduce memory
  # footprint and speed up applying the filters.
  preCrop: true

  # Forced 2D extracion:
  # This allows to calculate texture features using anisotropic voxels (although it assumes that voxels are isotropic
  # in-plane). This is an alternative to resampling the image to isotropic voxels.
  #force2D: true
  #force2Ddimension: 0  # axial slices, for coronal slices, use dimension 1 and for sagittal, dimension 2.

  # 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: 5

  # first order specific settings:
  # When normalizing, gray values below the mean will be negative. Shifting by 300 (3 StdDevs * 100) ensures that the
  # majority of voxels is positive (only outliers >3 SD lower than the mean will be negative).
  voxelArrayShift: 300

  # 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

#voxelSetting:
#  kernelRadius: 2
#  maskedKernel: true
#  initValue: nan