def get_mask(image, low_thresh=None, high_thresh=None, cleanup=2):
    """
    Get a binary mask image from the given image after thresholding

    ANTsR function: `getMask`

    Arguments
    ---------
    image : ANTsImage
        image from which mask will be computed. Can be an antsImage of 2, 3 or 4 dimensions.

    low_thresh : scalar (optional)
        An inclusive lower threshold for voxels to be included in the mask.
        If not given, defaults to image mean.

    high_thresh : scalar (optional)
        An inclusive upper threshold for voxels to be included in the mask.
        If not given, defaults to image max

    cleanup : integer
        If > 0, morphological operations will be applied to clean up the mask by eroding away small or weakly-connected areas, and closing holes.
        If cleanup is >0, the following steps are applied
            1. Erosion with radius 2 voxels
            2. Retain largest component
            3. Dilation with radius 1 voxel
            4. Morphological closing

    Returns
    -------
    ANTsImage

    Example
    -------
    >>> import ants
    >>> image = ants.image_read( ants.get_ants_data('r16') )
    >>> mask = ants.get_mask(image)
    """
    cleanup = int(cleanup)
    if isinstance(image, iio.ANTsImage):
        if image.pixeltype != 'float':
            image = image.clone('float')

    if low_thresh is None:
        low_thresh = image.mean()
    if high_thresh is None:
        high_thresh = image.max()

    mask_image = threshold_image(image, low_thresh, high_thresh)
    if cleanup > 0:
        mask_image = iMath(mask_image, 'ME', cleanup)
        mask_image = iMath(mask_image, 'GetLargestComponent')
        mask_image = iMath(mask_image, 'MD', cleanup)
        mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )
        while ((mask_image.min() == mask_image.max()) and (cleanup > 0)):
            cleanup = cleanup - 1
            mask_image = threshold_image(image, low_thresh, high_thresh)
            if cleanup > 0:
                mask_image = iMath(mask_image, 'ME', cleanup)
                mask_image = iMath(mask_image, 'MD', cleanup)
                mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )

            #if cleanup == 0:
            #    clustlab = label_clusters(mask_image, 1)
            #    mask_image = threshold_image(clustlab, 1, 1)

    return mask_image
Get a binary mask image from the given image after thresholding

    ANTsR function: `getMask`

    Arguments
    ---------
    image : ANTsImage
        image from which mask will be computed. Can be an antsImage of 2, 3 or 4 dimensions.

    low_thresh : scalar (optional)
        An inclusive lower threshold for voxels to be included in the mask.
        If not given, defaults to image mean.

    high_thresh : scalar (optional)
        An inclusive upper threshold for voxels to be included in the mask.
        If not given, defaults to image max

    cleanup : integer
        If > 0, morphological operations will be applied to clean up the mask by eroding away small or weakly-connected areas, and closing holes.
        If cleanup is >0, the following steps are applied
            1. Erosion with radius 2 voxels
            2. Retain largest component
            3. Dilation with radius 1 voxel
            4. Morphological closing

    Returns
    -------
    ANTsImage

    Example
    -------
    >>> import ants
    >>> image = ants.image_read( ants.get_ants_data('r16') )
    >>> mask = ants.get_mask(image)