Merge pull request AIM-Harvard#321 from JoostJM/duplicate_features

Remove duplicate features
Ougenny · Oct 30, 2017 · b5e6081 · b5e6081
2 parents 91f3820 + 18ffb41
commit b5e6081
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diff --git a/data/baseline/baseline_gldm.csv b/data/baseline/baseline_gldm.csv
@@ -1,6 +1,6 @@
-Patient ID,general_info_BoundingBox,general_info_GeneralSettings,general_info_ImageHash,general_info_ImageSpacing,general_info_InputImages,general_info_MaskHash,general_info_Version,general_info_VolumeNum,general_info_VoxelNum,GrayLevelVariance,HighGrayLevelEmphasis,GrayLevelNonUniformityNormalized,DependenceEntropy,DependenceNonUniformity,GrayLevelNonUniformity,SmallDependenceEmphasis,DependenceNonUniformityNormalized,DependenceVariance,LargeDependenceEmphasis,LargeDependenceLowGrayLevelEmphasis,SmallDependenceHighGrayLevelEmphasis,LargeDependenceHighGrayLevelEmphasis,SmallDependenceLowGrayLevelEmphasis,LowGrayLevelEmphasis
-brain1,"(162, 84, 11, 47, 70, 7)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{},9dc2c3137b31fd872997d92c9a92d5178126d9d3,1.2.0.post17.dev0+g7f21de9,2,4137,39.192714199063978,280.40657481266618,0.045156963555862191,6.8850198992694578,936.66014019821125,186.8143582306019,0.37960167130711403,0.22641047623838803,2.1619872286911961,8.6615905245346863,0.076505907367108272,110.30563945728201,2335.0519700265895,0.0035453562622343696,0.0086002740947983703
-brain2,"(205, 155, 8, 20, 15, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",f2b8fbc4d5d1da08a1a70e79a301f8a830139438,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{},b41049c71633e194bee4891750392b72eabd8800,1.2.0.post17.dev0+g7f21de9,1,453,16.831308568337644,278.15673289183223,0.095965576558533008,6.0278542914239788,77.860927152317885,43.472406181015451,0.31436421901358041,0.17187842638480769,3.1535848817546985,12.938189845474614,0.064771635420059245,98.737453271963673,3072.9955849889625,0.0039196112868640949,0.007352909129511827
-breast1,"(21, 64, 8, 9, 12, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",016951a8f9e8e5de21092d9d62b77262f92e04a5,"(0.664062, 0.664062, 2.1)",{},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,1.2.0.post17.dev0+g7f21de9,1,143,0.4749376497628246,3.5804195804195804,0.38901657782776666,4.6617281966026489,13.251748251748252,55.629370629370626,0.031210986272908942,0.092669568194043719,11.048266418895789,72.062937062937067,27.0475912975913,0.081947866184390011,295.91608391608389,0.022342990461955925,0.51806526806526798
-lung1,"(206, 347, 32, 24, 26, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",34dca4200809a5e76c702d6b9503d958093057a3,"(0.5703125, 0.5703125, 5.0)",{},054d887740012177bd1f9031ddac2b67170af0f3,1.2.0.post17.dev0+g7f21de9,1,837,24.733677910384273,383.9199522102748,0.07442664462743856,6.5503998915504766,120.97610513739546,62.29510155316607,0.31818600342693032,0.14453537053452264,18.002914773562633,37.449223416965353,0.075562483556322932,84.051168586019344,20425.035842293906,0.003630174681129408,0.0056058622732408228
-lung2,"(318, 333, 15, 87, 66, 11)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",14f57fd04838eb8c9cca2a0dd871d29971585975,"(0.6269531, 0.6269531, 5.0)",{},e284ff05593bc6cb2747261882e452d4efbccb3a,1.2.0.post17.dev0+g7f21de9,1,24644,15.086611671909003,1226.7050803441,0.28153721195636611,6.5037423957040241,1074.54609641292,6938.2030514526859,0.11508039120847749,0.043602746973418274,53.916928452166815,191.3726667748742,0.14681846709509599,97.930584534151734,250067.02475247523,0.0002186069898830078,0.00093448503739726116
+Patient ID,general_info_BoundingBox,general_info_GeneralSettings,general_info_ImageHash,general_info_ImageSpacing,general_info_InputImages,general_info_MaskHash,general_info_Version,general_info_VolumeNum,general_info_VoxelNum,GrayLevelVariance,HighGrayLevelEmphasis,DependenceEntropy,DependenceNonUniformity,GrayLevelNonUniformity,SmallDependenceEmphasis,DependenceNonUniformityNormalized,DependenceVariance,LargeDependenceEmphasis,LargeDependenceLowGrayLevelEmphasis,SmallDependenceHighGrayLevelEmphasis,LargeDependenceHighGrayLevelEmphasis,SmallDependenceLowGrayLevelEmphasis,LowGrayLevelEmphasis
+brain1,"(162, 84, 11, 47, 70, 7)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{},9dc2c3137b31fd872997d92c9a92d5178126d9d3,1.2.0.post17.dev0+g7f21de9,2,4137,39.1927142,280.4065748,6.885019899,936.6601402,186.8143582,0.379601671,0.226410476,2.161987229,8.661590525,0.076505907,110.3056395,2335.05197,0.003545356,0.008600274
+brain2,"(205, 155, 8, 20, 15, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",f2b8fbc4d5d1da08a1a70e79a301f8a830139438,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{},b41049c71633e194bee4891750392b72eabd8800,1.2.0.post17.dev0+g7f21de9,1,453,16.83130857,278.1567329,6.027854291,77.86092715,43.47240618,0.314364219,0.171878426,3.153584882,12.93818985,0.064771635,98.73745327,3072.995585,0.003919611,0.007352909
+breast1,"(21, 64, 8, 9, 12, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",016951a8f9e8e5de21092d9d62b77262f92e04a5,"(0.664062, 0.664062, 2.1)",{},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,1.2.0.post17.dev0+g7f21de9,1,143,0.47493765,3.58041958,4.661728197,13.25174825,55.62937063,0.031210986,0.092669568,11.04826642,72.06293706,27.0475913,0.081947866,295.9160839,0.02234299,0.518065268
+lung1,"(206, 347, 32, 24, 26, 3)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",34dca4200809a5e76c702d6b9503d958093057a3,"(0.5703125, 0.5703125, 5.0)",{},054d887740012177bd1f9031ddac2b67170af0f3,1.2.0.post17.dev0+g7f21de9,1,837,24.73367791,383.9199522,6.550399892,120.9761051,62.29510155,0.318186003,0.144535371,18.00291477,37.44922342,0.075562484,84.05116859,20425.03584,0.003630175,0.005605862
+lung2,"(318, 333, 15, 87, 66, 11)","{'distances': [1], 'voxelArrayShift': 2000, 'additionalInfo': True, 'enableCExtensions': True, 'weightingNorm': None, 'force2D': False, 'interpolator': None, 'resampledPixelSpacing': None, 'gldm_a': 0, 'label': 1, 'normalizeScale': 1, 'normalize': False, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'binWidth': 25, 'minimumROIDimensions': 1, 'symmetricalGLCM': False, 'padDistance': 5}",14f57fd04838eb8c9cca2a0dd871d29971585975,"(0.6269531, 0.6269531, 5.0)",{},e284ff05593bc6cb2747261882e452d4efbccb3a,1.2.0.post17.dev0+g7f21de9,1,24644,15.08661167,1226.70508,6.503742396,1074.546096,6938.203051,0.115080391,0.043602747,53.91692845,191.3726668,0.146818467,97.93058453,250067.0248,0.000218607,0.000934485
diff --git a/docs/features.rst b/docs/features.rst
@@ -9,11 +9,11 @@ subdivided into the following classes:
 
 * :ref:`radiomics-firstorder-label` (19 features)
 * :ref:`radiomics-shape-label` (16 features)
-* :ref:`radiomics-glcm-label` (26 features)
+* :ref:`radiomics-glcm-label` (23 features)
 * :ref:`radiomics-glszm-label` (16 features)
 * :ref:`radiomics-glrlm-label` (16 features)
 * :ref:`radiomics-ngtdm-label` (5 features)
-* :ref:`radiomics-gldm-label` (15 features)
+* :ref:`radiomics-gldm-label` (14 features)
 
 All feature classes, with the exception of shape can be calculated on either the original image and/or a derived image,
 obtained by applying one of several filters. The shape descriptors are independent of gray value, and are extracted from

diff --git a/docs/removedfeatures.rst b/docs/removedfeatures.rst
@@ -14,8 +14,8 @@ For included features and class definition, see :ref:`radiomics-glcm-label`.
 
 .. _radiomics-excluded-sumvariance-label:
 
-1. Sum Variance
-###############
+Sum Variance
+############
 
 .. math::
     \textit{sum variance} = \displaystyle\sum^{2N_g}_{k=2}{(k-SA)^2p_{x+y}(k)}
@@ -80,9 +80,131 @@ The mathematical proof is as follows:
 (7) Combining (3.) and (6.) yields the following formula:
 
 .. math::
-
     \text{Cluster Tendency} =
     \displaystyle\sum^{2N_g}_{k=2}{\Big[\big(k-SA\big)^2p_{x+y}(k)\Big]} =
     \textit{ sum variance}
 
 Q.E.D
+
+.. _radiomics-excluded-dissimilarity-label:
+
+Dissimilarity
+#############
+
+.. math::
+    \textit{dissimilarity} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)|i-j|}
+
+Dissimilarity is a measure of local intensity variation defined as the mean absolute difference between the
+neighbouring pairs. A larger value correlates with a greater disparity in intensity values
+among neighboring voxels.
+
+This feature has been removed, as it is mathematically identical to Difference Average (see
+:py:func:`~radiomics.glcm.RadiomicsGLCM.getDifferenceAverageFeatureValue()`).
+
+The mathematical proof is as follows:
+
+(1) As defined in GLCM, :math:`p_{x-y}(k) = \sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{p(i,j)},\text{ where }|i-j|=k`
+
+(2) Starting with Dissimilarity as defined in GLCM:
+
+.. math::
+    \textit{dissimilarity} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)|i-j|}
+
+    = \displaystyle\sum^{N_g-1}_{k=0}{\Big[
+    \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)|i-j|} \text{, where }|i-j|=k\Big]}
+
+    = \displaystyle\sum^{N_g-1}_{k=0}{\Big[
+    \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)k} \text{, where }|i-j|=k\Big]}
+
+(3) Using (1.) and (2.)
+
+.. math::
+    \textit{dissimilarity} = \displaystyle\sum^{N_g-1}_{k=0}{p_{x-y}(k)k} = \textit{difference average}
+
+Q.E.D.
+
+.. _radiomics-excluded-gldm-label:
+
+Excluded GLDM Features
+----------------------
+
+For included features and class definition, see :ref:`radiomics-gldm-label`.
+
+.. _radiomics-excluded-gldm-dependence-percentage-label:
+
+Dependence percentage
+#####################
+
+.. math::
+    \textit{dependence percentage} = \frac{N_z}{N_p}
+
+Dependence percentage is the ratio between voxels with a dependence zone and the total number of voxels in the image.
+Because PyRadiomics allows for incomplete dependence zones, all voxels have a dependence zone and :math:`N_z = N_p`.
+Therefore, this feature would always compute to 1.
+
+.. _radiomics-excluded-gldm-glnn-label:
+
+Gray Level Non-Uniformity Normalized
+####################################
+
+.. math::
+    \textit{GLNN} = \frac{\sum^{N_g}_{i=1}\left(\sum^{N_d}_{j=1}{\textbf{P}(i,j)}\right)^2}{N_z^2}
+
+Measures the similarity of gray-level intensity values in the image, where a lower GLNN value
+correlates with a greater similarity in intensity values. This is the normalized version of the GLN formula.
+
+This formula has been removed, because due to the definition of GLDM matrix (allowing incomplete zones), this feature is
+equal to first order Uniformity (see :py:func:`~radiomics.firstorder.RadiomicsFirstOrder.getUniformityFeatureValue()`).
+
+The mathematical proof is as follows:
+
+(1) Starting with Gray Level Non-Uniformity Normalized as defined in GLDM,
+
+.. math::
+    \textit{GLNN} = \frac{\sum^{N_g}_{i=1}\left(\sum^{N_d}_{j=1}{\textbf{P}(i,j)}\right)^2}{N_z^2}
+
+    = \displaystyle\sum^{N_g}_{i=1}{
+        \frac{ \left( \sum^{N_d}_{j=1}{ \textbf{P}(i,j) } \right)^2 }{ N_z^2 }
+    }
+
+    = \displaystyle\sum^{N_g}_{i=1}{ \left(
+        \frac{ \sum^{N_d}_{j=1}{ \textbf{P}(i,j) } }{ N_z }
+    \right)^2}
+
+    = \displaystyle\sum^{N_g}_{i=1}{ \left(
+        \sum^{N_d}_{j=1}{ \frac{ \textbf{P}(i,j) } { N_z } }
+    \right)^2}
+
+(2) As defined in GLDM, :math:`p(i,j) = \frac{\textbf{P}(i,j)}{N_z}`
+
+(3) Using (1.) and (2.)
+
+.. math::
+    \textit{GLNN} = \displaystyle\sum^{N_g}_{i=1}{ \left(
+        \sum^{N_d}_{j=1}{ p(i,j) }
+    \right)^2}
+
+(4) Because in the PyRadiomics definition incomplete dependence zones are allowed, every voxel in the ROI has a
+    dependence zone. Therefore, :math:`N_z = N_p` and :math:`\sum^{N_d}_{j=1}{\textbf{P}(i,j)}` equals the number of voxels
+    with gray level :math:`i` and is equal to :math:`\textbf{P}(i)`, the first order histogram with :math:`N_g` discreet
+    gray levels, as defined in first order.
+
+(5) As defined in first order, :math:`p(i) = \frac{\textbf{P}(i)}{N_p}`
+
+(6) Using (2.), (4.) and (5.)
+
+.. math::
+    \displaystyle\sum^{N_d}_{j=1}{\textbf{P}(i,j)} = \textbf{P}(i)
+
+    \frac{\sum^{N_d}_{j=1}{\textbf{P}(i,j)}}{N_z} = \frac{\textbf{P}(i)}{N_p}
+
+    \displaystyle\sum^{N_d}_{j=1}{\frac{\textbf{P}(i,j)}{N_z}} = \frac{\textbf{P}(i)}{N_p}
+
+    \displaystyle\sum^{N_d}_{j=1}{p(i,j)} = p(i)
+
+(7) Combining (3.) and (6.) yields:
+
+.. math::
+    \textit{GLNN} = \displaystyle\sum^{N_g}_{i=1}{p(i)^2} = Uniformity
+
+Q.E.D.
diff --git a/examples/exampleSettings/Full_shape_params.yaml b/examples/exampleSettings/Full_shape_params.yaml
@@ -0,0 +1,32 @@
+# This is an example of a parameters file with all shape features enabled, including those marked as redundant/deprecated:
+# - Compactness1
+# - Compactness2
+# - Spherical Disproportion
+
+setting:
+  label: 1
+  interpolator: 'sitkBSpline' # This is an enumerated value, here None is not allowed
+  resampledPixelSpacing: # This disables resampling, as it is interpreted as None, to enable it, specify spacing in x, y, z as [x, y , z]
+
+# "imageType" category is omitted, which will result in default values: only the "Original" (unfiltered) image is used, without additional custom settings
+
+# Featureclasses, from which features must be calculated. If a featureclass is not mentioned, no features are calculated
+# for that class. Otherwise, the specified features are calculated, or, if none are specified, all are calculated (excluding redundant/deprecated features).
+featureClass:
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
+    - 'Volume'
+    - 'SurfaceArea'
+    - 'SurfaceVolumeRatio'
+    - 'Compactness1'
+    - 'Compactness2'
+    - 'Sphericity'
+    - 'SphericalDisproportion'
+    - 'Maximum3DDiameter'
+    - 'Maximum2DDiameterSlice'
+    - 'Maximum2DDiameterColumn'
+    - 'Maximum2DDiameterRow'
+    - 'MajorAxis'
+    - 'MinorAxis'
+    - 'LeastAxis'
+    - 'Elongation'
+    - 'Flatness'
diff --git a/examples/exampleSettings/Params.yaml b/examples/exampleSettings/Params.yaml
@@ -29,23 +29,12 @@ imageType:
   Original: {} # for dictionaries / mappings, None values are not allowed, '{}' is interpreted as an empty dictionary
 
 # Featureclasses, from which features must be calculated. If a featureclass is not mentioned, no features are calculated
-# for that class. Otherwise, the specified features are calculated, or, if none are specified, all are calculated.
+# for that class. Otherwise, the specified features are calculated, or, if none are specified, all are calculated (excluding redundant/deprecated features).
 featureClass:
-  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
-    - 'Volume'
-    - 'SurfaceArea'
-    - 'SurfaceVolumeRatio'
-    - 'Sphericity'
-    - 'SphericalDisproportion'
-    - 'Maximum3DDiameter'
-    - 'Maximum2DDiameterSlice'
-    - 'Maximum2DDiameterColumn'
-    - 'Maximum2DDiameterRow'
-    - 'MajorAxis'
-    - 'MinorAxis'
-    - 'LeastAxis'
-    - 'Elongation'
-    - 'Flatness'
+  shape:
+  # 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: [] # specifying an empty list has the same effect as specifying nothing.
   glcm:  # Disable SumAverage by specifying all other GLCM features available
     - 'Autocorrelation'
@@ -58,11 +47,8 @@ featureClass:
     - 'DifferenceAverage'
     - 'DifferenceEntropy'
     - 'DifferenceVariance'
-    - 'Dissimilarity'
     - 'JointEnergy'
     - 'JointEntropy'
-    - 'Homogeneity1'
-    - 'Homogeneity2'
     - 'Imc1'
     - 'Imc2'
     - 'Idm'
@@ -75,3 +61,4 @@ featureClass:
     - 'SumSquares'
   glrlm: # for lists none values are allowed, in this case, all features are enabled
   glszm:
+  gldm:  # contains deprecated features, but as no individual features are specified, the deprecated features are not enabled
diff --git a/examples/exampleSettings/exampleCT.yaml b/examples/exampleSettings/exampleCT.yaml
@@ -14,21 +14,9 @@ imageType:
   Wavelet: {}
 
 featureClass:
-  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
-    - 'Volume'
-    - 'SurfaceArea'
-    - 'SurfaceVolumeRatio'
-    - 'Sphericity'
-    - 'SphericalDisproportion'
-    - 'Maximum3DDiameter'
-    - 'Maximum2DDiameterSlice'
-    - 'Maximum2DDiameterColumn'
-    - 'Maximum2DDiameterRow'
-    - 'MajorAxis'
-    - 'MinorAxis'
-    - 'LeastAxis'
-    - 'Elongation'
-    - 'Flatness'
+  # 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'
@@ -41,11 +29,8 @@ featureClass:
     - 'DifferenceAverage'
     - 'DifferenceEntropy'
     - 'DifferenceVariance'
-    - 'Dissimilarity'
     - 'JointEnergy'
     - 'JointEntropy'
-    - 'Homogeneity1'
-    - 'Homogeneity2'
     - 'Imc1'
     - 'Imc2'
     - 'Idm'
@@ -58,6 +43,7 @@ featureClass:
     - 'SumSquares'
   glrlm:
   glszm:
+  gldm:
 
 setting:
   # Normalization: