masking.py 文件源码

def median_otsu(unweighted_volume, median_radius=4, numpass=4, dilate=1):
    """ Simple brain extraction tool for dMRI data.

    This function is inspired from the ``median_otsu`` function from ``dipy``
    and is copied here to remove a dependency.

    It uses a median filter smoothing of the ``unweighted_volume``
    automatic histogram Otsu thresholding technique, hence the name
    *median_otsu*.

    This function is inspired from Mrtrix's bet which has default values
    ``median_radius=3``, ``numpass=2``. However, from tests on multiple 1.5T
    and 3T data. From GE, Philips, Siemens, the most robust choice is
    ``median_radius=4``, ``numpass=4``.

    Args:
        unweighted_volume (ndarray): ndarray of the unweighted volumes brain volumes
        median_radius (int): Radius (in voxels) of the applied median filter (default 4)
        numpass (int): Number of pass of the median filter (default 4)
        dilate (None or int): optional number of iterations for binary dilation

    Returns:
        ndarray: a 3D ndarray with the binary brain mask
    """
    b0vol = unweighted_volume

    logger = logging.getLogger(__name__)
    logger.info('We will use a single precision float type for the calculations.'.format())
    for env in mot.configuration.get_load_balancer().get_used_cl_environments(mot.configuration.get_cl_environments()):
        logger.info('Using device \'{}\'.'.format(str(env)))

    m = MedianFilter(median_radius)
    b0vol = m.filter(b0vol, nmr_of_times=numpass)

    thresh = _otsu(b0vol)
    mask = b0vol > thresh

    if dilate is not None:
        cross = generate_binary_structure(3, 1)
        mask = binary_dilation(mask, cross, iterations=dilate)

    return mask