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Revise Compactness 1 Formulas #218

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Mar 14, 2017
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Revise Compactness 1 Formulas #218

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dd7e831
MATH: Revise Compactness 1
JoostJM Mar 8, 2017
7290f88
DEL: Remove Roundness Feature
JoostJM Mar 9, 2017
ac234d0
TEST: Update baseline
JoostJM Mar 9, 2017
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15 changes: 13 additions & 2 deletions bin/Params.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,19 @@ inputImage:
# 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.
featureClass:
shape: # for lists none values are allowed, in this case, all features are enabled
shape: ['Volume',
'SurfaceArea',
'SurfaceVolumeRatio',
'Sphericity',
'SphericalDisproportion',
'Maximum3DDiameter',
'Maximum2DDiameterSlice',
'Maximum2DDiameterColumn',
'Maximum2DDiameterRow',
'Elongation',
'Flatness',
'Roundness'] # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
firstorder: [] # specifying an empty list has the same effect as specifying nothing.
glcm:
glcm: # for lists none values are allowed, in this case, all features are enabled
glrlm:
glszm:
12 changes: 6 additions & 6 deletions data/baseline/baseline_shape.csv
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Original file line number Diff line number Diff line change
@@ -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,Maximum3DDiameter,Compactness2,Maximum2DDiameterSlice,Sphericity,Compactness1,Elongation,SurfaceVolumeRatio,Volume,Flatness,SphericalDisproportion,Roundness,SurfaceArea,Maximum2DDiameterColumn,Maximum2DDiameterRow
brain1,(162; 84; 11; 47; 70; 7),{'verbose': False; 'voxelArrayShift': 2000; 'binWidth': 25; 'label': 1; 'interpolator': None; 'symmetricalGLCM': False; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'padDistance': 5},5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,(0.7812499999999999; 0.7812499999999999; 6.499999999999998),{'original': {}},9dc2c3137b31fd872997d92c9a92d5178126d9d3,0.post403.dev0+gd611f07,2,4137,65.53661458728622,0.11412770190085314,47.218791363317415,0.48506174422170256,26.754678721506981,1.7789885567018646,0.39230826186319256,16412.65869140624,1.2191850589688844,2.0615932134671486,0.6146906661500379,6438.8216037794018,44.548790405155771,61.580176713472483
brain2,(205; 155; 8; 20; 15; 3),{'verbose': False; 'voxelArrayShift': 2000; 'binWidth': 25; 'label': 1; 'interpolator': None; 'symmetricalGLCM': False; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'padDistance': 5},f2b8fbc4d5d1da08a1a70e79a301f8a830139438,(0.7812499999999999; 0.7812499999999999; 6.499999999999998),{'original': {}},b41049c71633e194bee4891750392b72eabd8800,0.post403.dev0+gd611f07,1,453,19.654138400092737,0.45448842446195314,15.566295972790057,0.76884880229711317,10.643947668070684,1.3499482848728939,0.51734387202502263,1797.1801757812489,1.1970744957871078,1.3006458448166522,1.0348881179911384,929.76015086528207,18.584714191036131,14.779664291265206
breast1,(21; 64; 8; 9; 12; 3),{'verbose': False; 'voxelArrayShift': 2000; 'binWidth': 25; 'label': 1; 'interpolator': None; 'symmetricalGLCM': False; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'padDistance': 5},016951a8f9e8e5de21092d9d62b77262f92e04a5,(0.664062; 0.664062; 2.1),{'original': {}},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,0.post403.dev0+gd611f07,1,143,7.772702166226878,0.38380439201415423,7.7726464321122961,0.72672479835852832,2.407442715594033,1.4286044695895188,1.3055392937755954,132.4257954551532,1.0230321043228265,1.3760367091624301,0.8556418393749154,172.88707947619218,5.3539199187231041,7.3046820000000006
lung1,(206; 347; 32; 24; 26; 3),{'verbose': False; 'voxelArrayShift': 2000; 'binWidth': 25; 'label': 1; 'interpolator': None; 'symmetricalGLCM': False; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'padDistance': 5},34dca4200809a5e76c702d6b9503d958093057a3,(0.5703125; 0.5703125; 5.0),{'original': {}},054d887740012177bd1f9031ddac2b67170af0f3,0.post403.dev0+gd611f07,1,837,18.182594712754316,0.43779658729410265,15.978930906357792,0.7593187496870597,9.0459132938754774,1.3912249269789807,0.57467140294582819,1361.1978149414062,1.3975132116994384,1.3169699818582548,1.031094246578549,782.2414579991738,16.044440540748841,13.537563480922259
lung2,(318; 333; 15; 87; 66; 11),{'verbose': False; 'voxelArrayShift': 2000; 'binWidth': 25; 'label': 1; 'interpolator': None; 'symmetricalGLCM': False; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'padDistance': 5},14f57fd04838eb8c9cca2a0dd871d29971585975,(0.6269531; 0.6269531; 5.0),{'original': {}},e284ff05593bc6cb2747261882e452d4efbccb3a,0.post403.dev0+gd611f07,1,24644,65.4490741679312,0.30580544578561636,55.235955786717163,0.67372356547818002,60.760123315042613,1.3452677171012488,0.19691793910543032,48434.10876246395,1.3057448083030738,1.4842882915788209,0.8768796050137607,9537.5448799126643,61.55885055061254,57.721196463628289
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,Maximum3DDiameter,Compactness2,Maximum2DDiameterSlice,Sphericity,Compactness1,Elongation,SurfaceVolumeRatio,Volume,Flatness,SphericalDisproportion,SurfaceArea,Maximum2DDiameterColumn,Maximum2DDiameterRow
brain1,(162; 84; 11; 47; 70; 7),{'verbose': False; 'voxelArrayShift': 2000; 'enableCExtensions': True; 'interpolator': None; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'normalizeScale': 1; 'normalize': False; 'additionalInfo': True; 'removeOutliers': None; 'binWidth': 25; 'label': 1; 'symmetricalGLCM': False; 'padDistance': 5},5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,(0.7812499999999999; 0.7812499999999999; 6.499999999999998),{'original': {}},9dc2c3137b31fd872997d92c9a92d5178126d9d3,0.post403.dev0+gd611f07,2,4137,65.53661458728622,0.11412770190085314,47.218791363317415,0.48506174422170256,0.017922327666112708,1.7789885567018646,0.39230826186319256,16412.65869140624,1.2191850589688844,2.0615932134671486,6438.8216037794018,44.548790405155771,61.580176713472483
brain2,(205; 155; 8; 20; 15; 3),{'verbose': False; 'voxelArrayShift': 2000; 'enableCExtensions': True; 'interpolator': None; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'normalizeScale': 1; 'normalize': False; 'additionalInfo': True; 'removeOutliers': None; 'binWidth': 25; 'label': 1; 'symmetricalGLCM': False; 'padDistance': 5},f2b8fbc4d5d1da08a1a70e79a301f8a830139438,(0.7812499999999999; 0.7812499999999999; 6.499999999999998),{'original': {}},b41049c71633e194bee4891750392b72eabd8800,0.post403.dev0+gd611f07,1,453,19.654138400092737,0.45448842446195314,15.566295972790057,0.76884880229711317,0.035765169710140834,1.3499482848728939,0.51734387202502263,1797.1801757812489,1.1970744957871078,1.3006458448166522,929.76015086528207,18.584714191036131,14.779664291265206
breast1,(21; 64; 8; 9; 12; 3),{'verbose': False; 'voxelArrayShift': 2000; 'enableCExtensions': True; 'interpolator': None; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'normalizeScale': 1; 'normalize': False; 'additionalInfo': True; 'removeOutliers': None; 'binWidth': 25; 'label': 1; 'symmetricalGLCM': False; 'padDistance': 5},016951a8f9e8e5de21092d9d62b77262f92e04a5,(0.664062; 0.664062; 2.1),{'original': {}},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,0.post403.dev0+gd611f07,1,143,7.772702166226878,0.38380439201415423,7.7726464321122961,0.72672479835852832,0.032866529447821202,1.4286044695895188,1.3055392937755954,132.4257954551532,1.0230321043228265,1.3760367091624301,172.88707947619218,5.3539199187231041,7.3046820000000006
lung1,(206; 347; 32; 24; 26; 3),{'verbose': False; 'voxelArrayShift': 2000; 'enableCExtensions': True; 'interpolator': None; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'normalizeScale': 1; 'normalize': False; 'additionalInfo': True; 'removeOutliers': None; 'binWidth': 25; 'label': 1; 'symmetricalGLCM': False; 'padDistance': 5},34dca4200809a5e76c702d6b9503d958093057a3,(0.5703125; 0.5703125; 5.0),{'original': {}},054d887740012177bd1f9031ddac2b67170af0f3,0.post403.dev0+gd611f07,1,837,18.182594712754316,0.43779658729410265,15.978930906357792,0.7593187496870597,0.035102258719353144,1.3912249269789807,0.57467140294582819,1361.1978149414062,1.3975132116994384,1.3169699818582548,782.2414579991738,16.044440540748841,13.537563480922259
lung2,(318; 333; 15; 87; 66; 11),{'verbose': False; 'voxelArrayShift': 2000; 'enableCExtensions': True; 'interpolator': None; 'resampledPixelSpacing': None; 'gldm_a': 0; 'weightingNorm': None; 'normalizeScale': 1; 'normalize': False; 'additionalInfo': True; 'removeOutliers': None; 'binWidth': 25; 'label': 1; 'symmetricalGLCM': False; 'padDistance': 5},14f57fd04838eb8c9cca2a0dd871d29971585975,(0.6269531; 0.6269531; 5.0),{'original': {}},e284ff05593bc6cb2747261882e452d4efbccb3a,0.post403.dev0+gd611f07,1,24644,65.4490741679312,0.30580544578561636,55.235955786717163,0.67372356547818002,0.029337390690641122,1.3452677171012488,0.19691793910543032,48434.10876246395,1.3057448083030738,1.4842882915788209,9537.5448799126643,61.55885055061254,57.721196463628289
114 changes: 65 additions & 49 deletions radiomics/shape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,15 +8,14 @@
class RadiomicsShape(base.RadiomicsFeaturesBase):
r"""
In this group of features we included descriptors of the three-dimensional size and shape of the tumor region.
Let in the following definitions denote :math:`V` the volume and :math:`A` the surface area of the volume of interest.
Let in the following definitions denote :math:`V` the volume (mm\ :sup:`3`) and :math:`A` the surface area of the
volume (mm\ :sup:`2`) of interest.
"""

def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsShape, self).__init__(inputImage, inputMask, **kwargs)

self.pixelSpacing = numpy.array(inputImage.GetSpacing()[::-1])
z, x, y = self.pixelSpacing
self.cubicMMPerVoxel = z * x * y

# Use SimpleITK for some shape features
self.lssif = sitk.LabelShapeStatisticsImageFilter()
Expand Down Expand Up @@ -163,13 +162,13 @@ def getVolumeFeatureValue(self):

def getSurfaceAreaFeatureValue(self):
r"""
Calculate the surface area of the tumor region in square millimeters.
Calculate the surface area of the tumor region in square millimeters using a marching cubes algorithm.

:math:`A = \displaystyle\sum^{N}_{i=1}{\frac{1}{2}|\textbf{a}_i\textbf{b}_i \times \textbf{a}_i\textbf{c}_i|}`

Where:

:math:`N` is the number of triangles forming the surface of the volume
:math:`N` is the number of triangles forming the surface mesh of the volume (ROI)

:math:`a_ib_i` and :math:`a_ic_i` are the edges of the :math:`i`\ :sup:`th` triangle formed by points :math:`a_i`,
:math:`b_i` and :math:`c_i`
Expand All @@ -182,36 +181,85 @@ def getSurfaceVolumeRatioFeatureValue(self):
Calculate the surface area to volume ratio of the tumor region

:math:`surface\ to\ volume\ ratio = \frac{A}{V}`

Here, a lower value indicates a more compact (sphere-like) shape.
"""
return (self.SurfaceArea / self.Volume)

def getSphericityFeatureValue(self):
r"""
Calculate the Sphericity of the tumor region.

:math:`sphericity = \frac{\sqrt[3]{36 \pi V^2}}{A}`

Sphericity is a measure of the roundness of the shape of the tumor region relative to a sphere. It is a
dimensionless measure, independent of scale and orientation. The value range is :math:`0 < sphericity \leq 1`, where
a value of 1 indicates a perfect sphere (a sphere has the smallest possible surface area for a given volume,
compared to other solids).

**N.B. This feature is correlated to Compactness 1, Compactness 2 and Spherical Disproportion. In the default
parameter file provided in the** ``pyradiomics\\bin`` **folder, only Compactness 1 and Compactness 2 are therefore
disabled.**
"""
return (36 * numpy.pi * self.Volume ** 2) ** (1.0 / 3.0) / self.SurfaceArea

def getCompactness1FeatureValue(self):
r"""
Calculate the compactness (1) of the tumor region.

:math:`compactness\ 1 = \frac{V}{\sqrt{\pi}A^{\frac{2}{3}}}`
:math:`compactness\ 1 = \frac{V}{\sqrt{\pi A^3}}`

Similar to Sphericity, Compactness 1 is a measure of how compact the shape of the tumor is relative to a sphere
(most compact). It is therefore correlated to Sphericity and redundant. It is provided here for completeness.
The value range is :math:`0 < compactness\ 1 \leq \frac{1}{6 \pi}`, where a value of :math:`\frac{1}{6 \pi}`
indicates a perfect sphere.

By definition, :math:`compactness\ 1 = \frac{1}{6 \pi}\sqrt{compactness\ 2} =
\frac{1}{6 \pi}\sqrt{sphericity^3}`.

Compactness 1 is a measure of how compact the shape of the tumor is
relative to a sphere (most compact). It is a dimensionless measure,
independent of scale and orientation. Compactness 1 is defined as the
ratio of volume to the :math:`\sqrt{\text{surface area}^3}`. This is a measure of the
compactness of the shape of the image ROI
**N.B. This feature is correlated to Compactness 2, Sphericity and Spherical Disproportion. In the default
parameter file provided in the** ``pyradiomics\\bin`` **folder, only Compactness 1 and Compactness 2 are therefore
disabled.**
"""
return ((self.Volume) / ((self.SurfaceArea) ** (2.0 / 3.0) * numpy.sqrt(numpy.pi)))
return ((self.Volume) / ((self.SurfaceArea) ** (3.0 / 2.0) * numpy.sqrt(numpy.pi)))

def getCompactness2FeatureValue(self):
r"""
Calculate the Compactness (2) of the tumor region.

:math:`compactness\ 2 = 36\pi\frac{V^2}{A^3}`
:math:`compactness\ 2 = 36 \pi \frac{V^2}{A^3}`

Compactness 2 is a measure of how compact the shape of the tumor is
relative to a sphere (most compact). It is a dimensionless measure,
independent of scale and orientation. This is a measure of the compactness
of the shape of the image ROI.
Similar to Sphericity and Compactness 1, Compactness 2 is a measure of how compact the shape of the tumor is
relative to a sphere (most compact). It is a dimensionless measure, independent of scale and orientation. The value
range is :math:`0 < compactness\ 2 \leq 1`, where a value of 1 indicates a perfect sphere.

By definition, :math:`compactness\ 2 = (sphericity)^3`

**N.B. This feature is correlated to Compactness 1, Sphericity and Spherical Disproportion. In the default
parameter file provided in the** ``pyradiomics\\bin`` **folder, only Compactness 1 and Compactness 2 are therefore
disabled.**
"""
return ((36.0 * numpy.pi) * ((self.Volume) ** 2.0) / ((self.SurfaceArea) ** 3.0))

def getSphericalDisproportionFeatureValue(self):
r"""
Calculate the Spherical Disproportion of the tumor region.

:math:`spherical\ disproportion = \frac{A}{4\pi R^2} = \frac{A}{\sqrt[3]{36 \pi V^2}}`

Where :math:`R` is the radius of a sphere with the same volume as the tumor, and equal to
:math:`\sqrt[3]{\frac{3V}{4\pi}}`.

Spherical Disproportion is the ratio of the surface area of the tumor region to the surface area of a sphere with
the same volume as the tumor region, and by definition, the inverse of Sphericity. Therefore, the value range is
:math:`spherical\ disproportion \geq 1`, with a value of 1 indicating a perfect sphere.

**N.B. This feature is correlated to Compactness 1, Sphericity and Spherical Disproportion. In the default
parameter file provided in the** ``pyradiomics\\bin`` **folder, only Compactness 1 and Compactness 2 are therefore
disabled.**
"""
return self.SurfaceArea / (36 * numpy.pi * self.Volume ** 2) ** (1.0 / 3.0)

def getMaximum3DDiameterFeatureValue(self):
r"""
Calculate the largest pairwise euclidean distance between tumor surface voxels.
Expand Down Expand Up @@ -240,32 +288,6 @@ def getMaximum2DDiameterRowFeatureValue(self):

return self._getMaximum2Ddiameter(2)

def getSphericalDisproportionFeatureValue(self):
r"""
Calculate the Spherical Disproportion of the tumor region.

:math:`spherical\ disproportion = \frac{A}{4\pi R^2}`

Where :math:`R` is the radius of a sphere with the same volume as the tumor.

Spherical Disproportion is the ratio of the surface area of the
tumor region to the surface area of a sphere with the same
volume as the tumor region.
"""
R = self.lssif.GetEquivalentSphericalRadius(self.label)
return ((self.SurfaceArea) / (4.0 * numpy.pi * (R ** 2.0)))

def getSphericityFeatureValue(self):
r"""
Calculate the Sphericity of the tumor region.

:math:`sphericity = \frac{\pi^{\frac{1}{3}}(6V)^{\frac{2}{3}}}{A}`

Sphericity is a measure of the roundness of the shape of the tumor region
relative to a sphere. This is another measure of the compactness of a tumor.
"""
return (((numpy.pi) ** (1.0 / 3.0) * (6.0 * self.Volume) ** (2.0 / 3.0)) / (self.SurfaceArea))

def getElongationFeatureValue(self):
"""

Expand All @@ -278,12 +300,6 @@ def getFlatnessFeatureValue(self):
"""
return self.lssif.GetFlatness(self.label)

def getRoundnessFeatureValue(self):
"""

"""
return self.lssif.GetRoundness(self.label)

def _interpolate(self, grid, p1, p2):
diff = (.5 - self.maskArray[tuple(grid[p1])]) / (self.maskArray[tuple(grid[p2])] - self.maskArray[tuple(grid[p1])])
return (grid[p1] + ((grid[p2] - grid[p1]) * diff)) * self.pixelSpacing
Expand Down