def volume_change_correlation(results, references):
    r"""
    Volume change correlation.
    
    Computes the linear correlation of change 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.
        
    """
    results = numpy.atleast_2d(numpy.array(results).astype(numpy.bool))
    references = numpy.atleast_2d(numpy.array(references).astype(numpy.bool))
    
    results_volumes = numpy.asarray([numpy.count_nonzero(r) for r in results])
    references_volumes = numpy.asarray([numpy.count_nonzero(r) for r in references])
    
    results_volumes_changes = results_volumes[1:] - results_volumes[:-1]
    references_volumes_changes = references_volumes[1:] - references_volumes[:-1] 
    
    return pearsonr(results_volumes_changes, references_volumes_changes)