class_docstr: This class enables the extraction of image features (Harilick
  features) and basic statistics over a segmentation
class_docstr_tok: ["This","class","enables","the","extraction","of","image","features","(","Harilick","features",")","and","basic","statistics","over","a","segmentation"]
docstr: Creates the vector of harilick features for one glcm matrix.
  Definition of the Harilick features can be found in
  
      Textural features for image classification
      Robert Harilick K, Shanmugam and Its'Hak Dinstein
      in IEEE Transactions on systems, man and cybernetics
      Vol SMC-3 issue 6 pp610-621
  
  :param matrix: glcm matrix on which to calculates the Harilick features
  :return:
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code: def harilick(self, matrix):
      """
      Creates the vector of harilick features for one glcm matrix.
      Definition of the Harilick features can be found in
  
          Textural features for image classification
          Robert Harilick K, Shanmugam and Its'Hak Dinstein
          in IEEE Transactions on systems, man and cybernetics
          Vol SMC-3 issue 6 pp610-621
  
      :param matrix: glcm matrix on which to calculates the Harilick features
      :return:
      """
      vector_harilick = []
      asm = self.angular_second_moment(matrix)
      contrast = self.contrast(matrix)
      correlation = self.correlation(matrix)
      sum_square = self.sum_square_variance(matrix)
      sum_average = self.sum_average(matrix)
      idifferentmomment = self.inverse_difference_moment(matrix)
      sum_entropy = self.sum_entropy(matrix)
      entropy = self.entropy(matrix)
      (
          differencevariance,
          differenceentropy,
      ) = self.difference_variance_entropy(matrix)
      sum_variance = self.sum_variance(matrix)
      imc1, imc2 = self.information_measure_correlation(matrix)
      vector_harilick.append(asm)
      vector_harilick.append(contrast)
      vector_harilick.append(correlation)
      vector_harilick.append(sum_square)
      vector_harilick.append(sum_average)
      vector_harilick.append(idifferentmomment)
      vector_harilick.append(sum_entropy)
      vector_harilick.append(entropy)
      vector_harilick.append(differencevariance)
      vector_harilick.append(differenceentropy)
      vector_harilick.append(sum_variance)
      vector_harilick.append(imc1)
      vector_harilick.append(imc2)
      return vector_harilick
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import numpy.ma
  import math
  import scipy.stats.mstats
  import scipy.ndimage
  from niftynet.utilities.util_common import MorphologyOps, CacheFunctionOutput
label_desc: Use image node_id 21 for calling the RegionProperties obj's underlying member method code with example usage: obj.harilick(matrix) and returns: vector_harilick