Function remove_small_objects

monai/transforms/utils.py:1421–1492

Use `skimage.morphology.remove_small_objects` to remove small objects from images. See: https://scikit-image.org/docs/dev/api/skimage.morphology.html#remove-small-objects. Data should be one-hotted. Args: img: image to process. Expected shape: C, H,W,[D]. Expected to only

(
    img: NdarrayTensor,
    min_size: int = 64,
    connectivity: int = 1,
    independent_channels: bool = True,
    by_measure: bool = False,
    pixdim: Sequence[float] | float | np.ndarray | None = None,
)

def remove_small_objects(
    img: NdarrayTensor,
    min_size: int = 64,
    connectivity: int = 1,
    independent_channels: bool = True,
    by_measure: bool = False,
    pixdim: Sequence[float] | float | np.ndarray | None = None,
) -> NdarrayTensor:
    """
    Use `skimage.morphology.remove_small_objects` to remove small objects from images.
    See: https://scikit-image.org/docs/dev/api/skimage.morphology.html#remove-small-objects.

    Data should be one-hotted.

    Args:
        img: image to process. Expected shape: C, H,W,[D]. Expected to only have singleton channel dimension,
            i.e., not be one-hotted. Converted to type int.
        min_size: objects smaller than this size are removed.
        connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
            Accepted values are ranging from  1 to input.ndim. If ``None``, a full
            connectivity of ``input.ndim`` is used. For more details refer to linked scikit-image
            documentation.
        independent_channels: Whether to consider each channel independently.
        by_measure: Whether the specified min_size is in number of voxels. if this is True then min_size
            represents a surface area or volume value of whatever units your image is in (mm^3, cm^2, etc.)
            default is False.
        pixdim: the pixdim of the input image. if a single number, this is used for all axes.
            If a sequence of numbers, the length of the sequence must be equal to the image dimensions.
    """
    # if all equal to one value, no need to call skimage
    if len(unique(img)) == 1:
        return img

    if not has_morphology:
        raise RuntimeError("Skimage required.")

    if by_measure:
        sr = len(img.shape[1:])
        if isinstance(img, monai.data.MetaTensor):
            _pixdim = img.pixdim
        elif pixdim is not None:
            _pixdim = ensure_tuple_rep(pixdim, sr)
        else:
            warnings.warn("`img` is not of type MetaTensor and `pixdim` is None, assuming affine to be identity.")
            _pixdim = (1.0,) * sr
        voxel_volume = np.prod(np.array(_pixdim))
        if voxel_volume == 0:
            warnings.warn("Invalid `pixdim` value detected, set it to 1. Please verify the pixdim settings.")
            voxel_volume = 1
        min_size = np.ceil(min_size / voxel_volume)
    elif pixdim is not None:
        warnings.warn("`pixdim` is specified but not in use when computing the volume.")

    img_np: np.ndarray
    img_np, *_ = convert_data_type(img, np.ndarray)

    # morphology.remove_small_objects assumes them to be independent by default
    # else, convert to foreground vs background, remove small objects, then convert