def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsGLRLM, self).__init__(inputImage, inputMask, **kwargs)
self.weightingNorm = kwargs.get('weightingNorm',
None) # manhattan, euclidean, infinity
self.coefficients = {}
self.P_glrlm = {}
# binning
self.matrix, self.binEdges = imageoperations.binImage(
self.binWidth, self.matrix, self.matrixCoordinates)
self.coefficients['Ng'] = int(
numpy.max(self.matrix[
self.matrixCoordinates])) # max gray level in the ROI
self.coefficients['Nr'] = numpy.max(self.matrix.shape)
self.coefficients['Np'] = self.targetVoxelArray.size
if cMatsEnabled():
self.P_glrlm = self._calculateCMatrix()
else:
self.P_glrlm = self._calculateMatrix()
self._calculateCoefficients()
self.logger.debug(
'Feature class initialized, calculated GLRLM with shape %s',
self.P_glrlm.shape)
def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsGLSZM, self).__init__(inputImage, inputMask, **kwargs)
self.coefficients = {}
self.P_glszm = {}
# binning
self.matrix, self.binEdges = imageoperations.binImage(
self.binWidth, self.matrix, self.matrixCoordinates)
self.coefficients['Ng'] = int(
numpy.max(self.matrix[
self.matrixCoordinates])) # max gray level in the ROI
self.coefficients['Np'] = self.targetVoxelArray.size
self.coefficients['grayLevels'] = numpy.unique(
self.matrix[self.matrixCoordinates])
if cMatsEnabled():
self.P_glszm = self._calculateCMatrix()
else:
self.P_glszm = self._calculateMatrix()
self._calculateCoefficients()
self.logger.debug(
'Feature class initialized, calculated GLSZM with shape %s',
self.P_glszm.shape)
def _applyBinning(self, matrix):
matrix, _ = imageoperations.binImage(matrix, self.maskArray,
**self.settings)
self.coefficients['grayLevels'] = numpy.unique(matrix[self.maskArray])
self.coefficients['Ng'] = int(
numpy.max(
self.coefficients['grayLevels'])) # max gray level in the ROI
return matrix
def _applyBinning(self):
self.matrix, self.binEdges = imageoperations.binImage(
self.binWidth, self.imageArray, self.maskArray)
self.coefficients['grayLevels'] = numpy.unique(
self.matrix[self.maskArray])
self.coefficients['Ng'] = int(
numpy.max(
self.coefficients['grayLevels'])) # max gray level in the ROI
def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsGLSZM, self).__init__(inputImage, inputMask, **kwargs)
self.coefficients = {}
self.P_glszm = {}
# binning
self.matrix, self.histogram = imageoperations.binImage(
self.binWidth, self.matrix, self.matrixCoordinates)
self.coefficients['Ng'] = self.histogram[1].shape[0] - 1
self.coefficients['Np'] = self.targetVoxelArray.size
self._calculateGLSZM()
self._calculateCoefficients()
def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsGLCM, self).__init__(inputImage, inputMask, **kwargs)
self.symmetricalGLCM = kwargs.get('symmetricalGLCM', True)
self.weightingNorm = kwargs.get('weightingNorm',
None) # manhattan, euclidean, infinity
self.coefficients = {}
self.P_glcm = {}
# binning
self.matrix, self.histogram = imageoperations.binImage(
self.binWidth, self.matrix, self.matrixCoordinates)
self.coefficients['Ng'] = self.histogram[1].shape[0] - 1
if cMatsEnabled():
self.P_glcm = self._calculateCMatrix()
else:
self.P_glcm = self._calculateMatrix()
self._calculateCoefficients()
def __init__(self, inputImage, inputMask, **kwargs):
super(RadiomicsGLRLM, self).__init__(inputImage, inputMask, **kwargs)
self.weightingNorm = kwargs.get('weightingNorm',
None) # manhattan, euclidean, infinity
self.coefficients = {}
self.P_glrlm = {}
# binning
self.matrix, self.histogram = imageoperations.binImage(
self.binWidth, self.matrix, self.matrixCoordinates)
self.coefficients['Ng'] = self.histogram[1].shape[0] - 1
self.coefficients['Nr'] = numpy.max(self.matrix.shape)
self.coefficients['Np'] = self.targetVoxelArray.size
if cMatsEnabled():
self.P_glrlm = self._calculateCMatrix()
else:
self.P_glrlm = self._calculateMatrix()
self._calculateCoefficients()
