Class ClipIntensityPercentiles

monai/transforms/intensity/array.py:1039–1181

Apply clip based on the intensity distribution of input image. If `sharpness_factor` is provided, the intensity values will be soft clipped according to f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)) From https://medium.com/life-

class ClipIntensityPercentiles(Transform):
    """
    Apply clip based on the intensity distribution of input image.
    If `sharpness_factor` is provided, the intensity values will be soft clipped according to
    f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv))
    From https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291

    Soft clipping preserves the order of the values and maintains the gradient everywhere.
    For example:

    .. code-block:: python
        :emphasize-lines: 11, 22

        image = torch.Tensor(
            [[[1, 2, 3, 4, 5],
              [1, 2, 3, 4, 5],
              [1, 2, 3, 4, 5],
              [1, 2, 3, 4, 5],
              [1, 2, 3, 4, 5],
              [1, 2, 3, 4, 5]]])

        # Hard clipping from lower and upper image intensity percentiles
        hard_clipper = ClipIntensityPercentiles(30, 70)
        print(hard_clipper(image))
        metatensor([[[2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.]]])


        # Soft clipping from lower and upper image intensity percentiles
        soft_clipper = ClipIntensityPercentiles(30, 70, 10.)
        print(soft_clipper(image))
        metatensor([[[2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000]]])

    See Also:

        - :py:class:`monai.transforms.ScaleIntensityRangePercentiles`
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        lower: float | None,
        upper: float | None,
        sharpness_factor: float | None = None,
        channel_wise: bool = False,
        return_clipping_values: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None: