def _calculateCMatrix(self):
self.logger.debug('Calculating GLRLM matrix in C')
P_glrlm, angles = cMatrices.calculate_glrlm(
self.matrix, self.maskArray, self.coefficients['Ng'],
self.coefficients['Nr'], self.kwargs.get('force2D', False),
self.kwargs.get('force2Ddimension', 0))
P_glrlm = self._applyMatrixOptions(P_glrlm, angles)
return P_glrlm
def _calculateMatrix(self):
self.logger.debug('Calculating GLRLM matrix in C')
P_glrlm, angles = cMatrices.calculate_glrlm(
self.matrix, self.maskArray, self.coefficients['Ng'],
self.coefficients['Nr'], self.settings.get('force2D', False),
self.settings.get('force2Ddimension', 0))
self.logger.debug('Process calculated matrix')
# Optionally apply a weighting factor
if self.weightingNorm is not None:
self.logger.debug('Applying weighting (%s)', self.weightingNorm)
# Correct the number of voxels for the number of times it is used (once per angle), affects run percentage
self.coefficients['Np'] *= len(angles)
pixelSpacing = self.inputImage.GetSpacing()[::-1]
weights = numpy.empty(len(angles))
for a_idx, a in enumerate(angles):
if self.weightingNorm == 'infinity':
weights[a_idx] = max(numpy.abs(a) * pixelSpacing)
elif self.weightingNorm == 'euclidean':
weights[a_idx] = numpy.sqrt(
numpy.sum((numpy.abs(a) * pixelSpacing)**2))
elif self.weightingNorm == 'manhattan':
weights[a_idx] = numpy.sum(numpy.abs(a) * pixelSpacing)
elif self.weightingNorm == 'no_weighting':
weights[a_idx] = 1
else:
self.logger.warning(
'weigthing norm "%s" is unknown, weighting factor is set to 1',
self.weightingNorm)
weights[a_idx] = 1
P_glrlm = numpy.sum(P_glrlm * weights[None, None, :],
2,
keepdims=True)
Nz = numpy.sum(P_glrlm, (0, 1))
# Delete empty angles if no weighting is applied
if P_glrlm.shape[2] > 1:
emptyAngles = numpy.where(Nz == 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_glrlm = numpy.delete(P_glrlm, emptyAngles, 2)
Nz = numpy.delete(Nz, emptyAngles, 0)
else:
self.logger.debug('No empty angles')
self.coefficients['Nz'] = Nz
return P_glrlm
def _calculateCMatrix(self):
self.logger.debug('Calculating GLRLM matrix in C')
Ng = self.coefficients['Ng']
Nr = self.coefficients['Nr']
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(self.matrixCoordinates, 1) + 1
angles = imageoperations.generateAngles(size, **self.kwargs)
P_glrlm = cMatrices.calculate_glrlm(self.matrix, self.maskArray, angles, Ng, Nr)
P_glrlm = self._applyMatrixOptions(P_glrlm, angles)
return P_glrlm
def _calculateCMatrix(self):
Ng = self.coefficients['Ng']
Nr = self.coefficients['Nr']
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(
self.matrixCoordinates, 1) + 1
angles = imageoperations.generateAngles(size)
P_glrlm = cMatrices.calculate_glrlm(self.matrix, self.maskArray,
angles, Ng, Nr)
P_glrlm = self._applyMatrixOptions(P_glrlm, angles)
return P_glrlm
def _calculateCMatrix(self):
self.logger.debug('Calculating GLRLM matrix in C')
Ng = self.coefficients['Ng']
Nr = self.coefficients['Nr']
size = numpy.max(self.matrixCoordinates, 1) - numpy.min(self.matrixCoordinates, 1) + 1
# Do not pass kwargs directly, as distances may be specified, which must be forced to [1] for this class
angles = imageoperations.generateAngles(size,
force2Dextraction=self.kwargs.get('force2D', False),
force2Ddimension=self.kwargs.get('force2Ddimension', 0))
P_glrlm = cMatrices.calculate_glrlm(self.matrix, self.maskArray, angles, Ng, Nr)
P_glrlm = self._applyMatrixOptions(P_glrlm, angles)
return P_glrlm
def _calculateMatrix(self, voxelCoordinates=None):
self.logger.debug('Calculating GLRLM matrix in C')
Ng = self.coefficients['Ng']
Nr = numpy.max(self.imageArray.shape)
matrix_args = [
self.imageArray, self.maskArray, Ng, Nr,
self.settings.get('force2D', False),
self.settings.get('force2Ddimension', 0)
]
if self.voxelBased:
matrix_args += [
self.settings.get('kernelRadius', 1), voxelCoordinates
]
P_glrlm, angles = cMatrices.calculate_glrlm(
*matrix_args) # shape (Nvox, Ng, Nr, Na)
self.logger.debug('Process calculated matrix')
# Delete rows 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_glrlm = numpy.delete(P_glrlm, emptyGrayLevels - 1, 1)
# 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] = max(numpy.abs(a) * pixelSpacing)
elif self.weightingNorm == 'euclidean':
weights[a_idx] = numpy.sqrt(
numpy.sum((numpy.abs(a) * pixelSpacing)**2))
elif self.weightingNorm == 'manhattan':
weights[a_idx] = numpy.sum(numpy.abs(a) * pixelSpacing)
elif self.weightingNorm == 'no_weighting':
weights[a_idx] = 1
else:
self.logger.warning(
'weigthing norm "%s" is unknown, weighting factor is set to 1',
self.weightingNorm)
weights[a_idx] = 1
P_glrlm = numpy.sum(P_glrlm * weights[None, None, None, :],
3,
keepdims=True)
Nr = numpy.sum(P_glrlm, (1, 2))
# Delete empty angles if no weighting is applied
if P_glrlm.shape[3] > 1:
emptyAngles = numpy.where(numpy.sum(Nr, 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_glrlm = numpy.delete(P_glrlm, emptyAngles, 3)
Nr = numpy.delete(Nr, emptyAngles, 1)
else:
self.logger.debug('No empty angles')
Nr[Nr == 0] = numpy.nan # set sum to numpy.spacing(1) if sum is 0?
self.coefficients['Nr'] = Nr
return P_glrlm