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"]
code: def __compute_mask(self):
      # TODO: check whether this works for probabilities type_str
      foreground_selector = np.where((self.seg > 0).reshape(-1))[0]
      probs = self.seg.reshape(-1)[foreground_selector]
      regions = np.zeros(
          (foreground_selector.shape[0], self.img_channels)
      )
      for i in np.arange(self.img_channels):
          regions[:, i] = self.img[..., 0, i].reshape(-1)[
              foreground_selector
          ]
      return regions, probs
  
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 2 for calling the RegionProperties obj's underlying member method code with example usage: obj.__compute_mask() and returns: regions,  probs