/
ct_parameters.yaml
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/
ct_parameters.yaml
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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: {}
Wavelet: {}
# LoG: {'sigma': [1.0, 2.0, 3.0, 4.0, 5.0]}
# LBP2D: {}
# LBP3D: {}
# Gradient: {}
# Logarithm: {}
Square: {}
# SquareRoot: {}
# 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.
glcm: # Disable SumAverage by specifying all other GLCM features available
# - 'DifferenceEntropy'
# - 'MaximumProbability'
glrlm:
# - 'RunLengthNonUniformity'
shape:
firstorder:
gldm:
glszm:
ngtdm:
setting:
# Normalization: True
# 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:
resampledPixelSpacing:
padDistance: # 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