histogram_matching in slice-wise manner.

mri_histogram_matching.py

def hist_match(im_src, im_tar, n_bins):

    # normalize both images
    src_max = im_src.max()
    im_src_n = min_max_norm(im_src)
    im_tar_n = min_max_norm(im_tar)

    # Get histogram
    hist_src, bins = np.histogram(im_src_n.flatten(), n_bins)
    hist_tar, bins = np.histogram(im_tar_n.flatten(), n_bins)

    # Normalize cumulative density function
    cdf_src = hist_src.cumsum()
    cdf_tar = hist_tar.cumsum()
    # cdf_src = cdf_src / cdf_src[-1]
    cdf_tar = cdf_tar / cdf_tar[-1]

    # Interpolation & reshape
    im_match = np.interp(im_src_n.flatten(), cdf_tar, bins[:-1]).astype(np.float32)
    im_match = im_match.reshape((im_src.shape[0], im_src.shape[1]))

    # TODO : Should I multiply src_max?
    im_match_n = min_max_norm(im_match)
    im_match_n = im_match_n * src_max

    return im_match_n