code: def volume_correlation(results, references):
      r
      results = numpy.atleast_2d(numpy.array(results).astype(numpy.bool))
      references = numpy.atleast_2d(numpy.array(references).astype(numpy.bool))
      
      results_volumes = [numpy.count_nonzero(r) for r in results]
      references_volumes = [numpy.count_nonzero(r) for r in references]
      
      return pearsonr(results_volumes, references_volumes)
docs: r"""
      Volume correlation.
      
      Computes the linear correlation in binary object volume between the
      contents of the successive binary images supplied. Measured through
      the Pearson product-moment correlation coefficient. 
      
      Parameters
      ----------
      results : sequence of array_like
          Ordered list of input data containing objects. Each array_like will be
          converted into binary: background where 0, object everywhere else.
      references : sequence of array_like
          Ordered list of input data containing objects. Each array_like will be
          converted into binary: background where 0, object everywhere else.
          The order must be the same as for ``results``.
      
      Returns
      -------
      r : float
          The correlation coefficient between -1 and 1.
      p : float
          The two-side p value.