anatomical.py 文件源码

def artifact_mask(imdata, airdata, distance, zscore=10.):
    """Computes a mask of artifacts found in the air region"""
    from statsmodels.robust.scale import mad

    if not np.issubdtype(airdata.dtype, np.integer):
        airdata[airdata < .95] = 0
        airdata[airdata > 0.] = 1

    bg_img = imdata * airdata
    if np.sum((bg_img > 0).astype(np.uint8)) < 100:
        return np.zeros_like(airdata)

    # Find the background threshold (the most frequently occurring value
    # excluding 0)
    bg_location = np.median(bg_img[bg_img > 0])
    bg_spread = mad(bg_img[bg_img > 0])
    bg_img[bg_img > 0] -= bg_location
    bg_img[bg_img > 0] /= bg_spread

    # Apply this threshold to the background voxels to identify voxels
    # contributing artifacts.
    qi1_img = np.zeros_like(bg_img)
    qi1_img[bg_img > zscore] = 1
    qi1_img[distance < .10] = 0

    # Create a structural element to be used in an opening operation.
    struc = nd.generate_binary_structure(3, 1)
    qi1_img = nd.binary_opening(qi1_img, struc).astype(np.uint8)
    qi1_img[airdata <= 0] = 0

    return qi1_img