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()