def _calculateCMatrix(self):
self.logger.debug('Calculating GLCM matrix in C')
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(
self.matrixCoordinates, 1) + 1
angles = imageoperations.generateAngles(size, **self.kwargs)
Ng = self.coefficients['Ng']
P_glcm = cMatrices.calculate_glcm(self.matrix, self.maskArray, angles,
Ng)
P_glcm = self._applyMatrixOptions(P_glcm, angles)
# Delete rows and columns that specify gray levels not present in the ROI
Ng = self.coefficients['Ng']
NgVector = range(1, Ng + 1) # All possible gray values
GrayLevels = self.coefficients[
'grayLevels'] # Gray values present in ROI
emptyGrayLevels = numpy.array(
list(set(NgVector) -
set(GrayLevels))) # Gray values NOT present in ROI
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 0)
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 1)
return P_glcm
def _calculateCMatrix(self):
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(
self.matrixCoordinates, 1) + 1
angles = imageoperations.generateAngles(size)
Ng = self.coefficients['Ng']
P_glcm = cMatrices.calculate_glcm(self.matrix, self.maskArray, angles,
Ng)
P_glcm = self._applyMatrixOptions(P_glcm, angles)
return P_glcm
def _calculateCMatrix(self):
self.logger.debug('Calculating GLCM matrix in C')
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(self.matrixCoordinates, 1) + 1
angles = imageoperations.generateAngles(size, **self.kwargs)
Ng = self.coefficients['Ng']
P_glcm = cMatrices.calculate_glcm(self.matrix, self.maskArray, angles, Ng)
P_glcm = self._applyMatrixOptions(P_glcm, angles)
return P_glcm
def _calculateCMatrix(self):
self.logger.debug('Calculating GLCM matrix in C')
Ng = self.coefficients['Ng']
P_glcm, angles = cMatrices.calculate_glcm(self.matrix,
self.maskArray,
numpy.array(self.kwargs.get('distances', [1])),
Ng,
self.kwargs.get('force2D', False),
self.kwargs.get('force2Ddimension', 0))
P_glcm = self._applyMatrixOptions(P_glcm, angles)
# Delete rows and columns that specify gray levels not present in the ROI
NgVector = range(1, Ng + 1) # All possible gray values
GrayLevels = self.coefficients['grayLevels'] # Gray values present in ROI
emptyGrayLevels = numpy.array(list(set(NgVector) - set(GrayLevels))) # Gray values NOT present in ROI
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 0)
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 1)
return P_glcm
def _calculateMatrix(self, voxelCoordinates=None):
r"""
Compute GLCMs for the input image for every direction in 3D.
Calculated GLCMs are placed in array P_glcm with shape (i/j, a)
i/j = total gray-level bins for image array,
a = directions in 3D (generated by imageoperations.generateAngles)
"""
self.logger.debug('Calculating GLCM matrix in C')
Ng = self.coefficients['Ng']
matrix_args = [
self.imageArray, self.maskArray,
numpy.array(self.settings.get('distances', [1])), Ng,
self.settings.get('force2D', False),
self.settings.get('force2Ddimension', 0)
]
if self.voxelBased:
matrix_args += [
self.settings.get('kernelRadius', 1), voxelCoordinates
]
P_glcm, angles = cMatrices.calculate_glcm(*matrix_args)
self.logger.debug('Process calculated matrix')
# Delete rows and columns that specify gray levels not present in the ROI
NgVector = range(1, Ng + 1) # All possible gray values
GrayLevels = self.coefficients[
'grayLevels'] # Gray values present in ROI
emptyGrayLevels = numpy.array(
list(set(NgVector) - set(GrayLevels)),
dtype=int) # Gray values NOT present in ROI
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 1)
P_glcm = numpy.delete(P_glcm, emptyGrayLevels - 1, 2)
# Optionally make GLCMs symmetrical for each angle
if self.symmetricalGLCM:
self.logger.debug('Create symmetrical matrix')
# Transpose and copy GLCM and add it to P_glcm. Numpy.transpose returns a view if possible, use .copy() to ensure
# a copy of the array is used and not just a view (otherwise erroneous additions can occur)
P_glcm += numpy.transpose(P_glcm, (0, 2, 1, 3)).copy()
# Optionally apply a weighting factor
if self.weightingNorm is not None:
self.logger.debug('Applying weighting (%s)', self.weightingNorm)
pixelSpacing = self.inputImage.GetSpacing()[::-1]
weights = numpy.empty(len(angles))
for a_idx, a in enumerate(angles):
if self.weightingNorm == 'infinity':
weights[a_idx] = numpy.exp(
-max(numpy.abs(a) * pixelSpacing)**2)
elif self.weightingNorm == 'euclidean':
weights[a_idx] = numpy.exp(-numpy.sum(
(numpy.abs(a) * pixelSpacing)**2)) # sqrt ^ 2 = 1
elif self.weightingNorm == 'manhattan':
weights[a_idx] = numpy.exp(
-numpy.sum(numpy.abs(a) * pixelSpacing)**2)
elif self.weightingNorm == 'no_weighting':
weights[a_idx] = 1
else:
self.logger.warning(
'weigthing norm "%s" is unknown, W is set to 1',
self.weightingNorm)
weights[a_idx] = 1
P_glcm = numpy.sum(P_glcm * weights[None, None, None, :],
3,
keepdims=True)
sumP_glcm = numpy.sum(P_glcm, (1, 2))
# Delete empty angles if no weighting is applied
if P_glcm.shape[3] > 1:
emptyAngles = numpy.where(numpy.sum(sumP_glcm, 0) == 0)
if len(emptyAngles[0]) > 0: # One or more angles are 'empty'
self.logger.debug('Deleting %d empty angles:\n%s',
len(emptyAngles[0]), angles[emptyAngles])
P_glcm = numpy.delete(P_glcm, emptyAngles, 3)
sumP_glcm = numpy.delete(sumP_glcm, emptyAngles, 1)
else:
self.logger.debug('No empty angles')
# Mark empty angles with NaN, allowing them to be ignored in feature calculation
sumP_glcm[sumP_glcm == 0] = numpy.nan
# Normalize each glcm
P_glcm /= sumP_glcm[:, None, None, :]
return P_glcm