anatomical.py 文件源码

def gradient_threshold(in_file, in_segm, thresh=1.0, out_file=None):
    """ Compute a threshold from the histogram of the magnitude gradient image """
    import os.path as op
    import numpy as np
    import nibabel as nb
    from scipy import ndimage as sim

    struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2)

    if out_file is None:
        fname, ext = op.splitext(op.basename(in_file))
        if ext == '.gz':
            fname, ext2 = op.splitext(fname)
            ext = ext2 + ext
        out_file = op.abspath('{}_gradmask{}'.format(fname, ext))

    imnii = nb.load(in_file)

    hdr = imnii.get_header().copy()
    hdr.set_data_dtype(np.uint8)  # pylint: disable=no-member

    data = imnii.get_data().astype(np.float32)

    mask = np.zeros_like(data, dtype=np.uint8)  # pylint: disable=no-member
    mask[data > 15.] = 1

    segdata = nb.load(in_segm).get_data().astype(np.uint8)
    segdata[segdata > 0] = 1
    segdata = sim.binary_dilation(segdata, struc, iterations=2, border_value=1).astype(np.uint8)  # pylint: disable=no-member
    mask[segdata > 0] = 1

    mask = sim.binary_closing(mask, struc, iterations=2).astype(np.uint8)  # pylint: disable=no-member
    # Remove small objects
    label_im, nb_labels = sim.label(mask)
    artmsk = np.zeros_like(mask)
    if nb_labels > 2:
        sizes = sim.sum(mask, label_im, list(range(nb_labels + 1)))
        ordered = list(reversed(sorted(zip(sizes, list(range(nb_labels + 1))))))
        for _, label in ordered[2:]:
            mask[label_im == label] = 0
            artmsk[label_im == label] = 1

    mask = sim.binary_fill_holes(mask, struc).astype(np.uint8)  # pylint: disable=no-member

    nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file)
    return out_file