docstr: Normalized Cross-Correlation Similarity metric.
  
  Parameters
  ----------
  dim : int (either 2 or 3)
      the dimension of the image domain
  sigma_diff : the standard deviation of the Gaussian smoothing kernel to
      be applied to the update field at each iteration
  radius : int
      the radius of the squared (cubic) neighborhood at each voxel to be
      considered to compute the cross correlation
docstr_tok: ["Normalized","Cross-Correlation","Similarity","metric",".","Parameters","--","--","--","--","--","dim",":","int","(","either","2","or","3",")","the","dimension","of","the","image","domain","sigma_diff",":","the","standard","deviation","of","the","Gaussian","smoothing","kernel","to","be","applied","to","the","update","field","at","each","iteration","radius",":","int","the","radius","of","the","squared","(","cubic",")","neighborhood","at","each","voxel","to","be","considered","to","compute","the","cross","correlation"]
code: def __init__(self, dim, sigma_diff=2.0, radius=4):
      r"""Normalized Cross-Correlation Similarity metric.
  
      Parameters
      ----------
      dim : int (either 2 or 3)
          the dimension of the image domain
      sigma_diff : the standard deviation of the Gaussian smoothing kernel to
          be applied to the update field at each iteration
      radius : int
          the radius of the squared (cubic) neighborhood at each voxel to be
          considered to compute the cross correlation
      """
      super(CCMetric, self).__init__(dim)
      self.sigma_diff = sigma_diff
      self.radius = radius
      self._connect_functions()
  
imports: import abc
  import numpy
  from numpy import gradient
  from scipy import ndimage
  from dipy.align import vector_fields
  from dipy.align import sumsqdiff
  from dipy.align import crosscorr
  from dipy.align import expectmax
  from dipy.align import floating
label_desc: Use image node_id 1 to create a new CCMetric object from inherited base classes: SimilarityMetric with example: obj = CCMetric(dim, sigma_diff, radius)