calc_vesselness_measure_image
===============================================================

.. py:function:: petpal.preproc.segmentation_tools.calc_vesselness_measure_image(input_image: ants.core.ANTsImage, sigma_min: float = 2.0, sigma_max: float = 8.0, alpha: float = 0.5, beta: float = 0.5, gamma: float = 5.0, morph_open_radius: int = 1, **hessian_func_kwargs) -> ants.core.ANTsImage

   Computes a vesselness measure image using Hessian-based objectness filtering.

   This function calculates the vesselness measure of a given 3D image using
   multi-scale Hessian filtering with specified parameters. We call the
   :func:`ants.hessian_objectness` after max-normalizing the input image.
   It enhances tubular structures like vessels, making them more pronounced
   in the output image. Optionally, a morphological opening operation can be
   applied to the result to refine the output and remove pepper-like artefacts.

   From the docs of :func:`ants.hessian_objectness`:
   '
   Based on the paper by Westin et al., "Geometrical
   Diffusion Measures for MRI from Tensor Basis Analysis" and Luca Antiga's
   Insight Journal paper http://hdl.handle.net/1926/576.
   '

   :param input_image: Input 3D image for vesselness computation.
   :type input_image: ants.core.ANTsImage
   :param sigma_min: Minimum scale for multi-scale Hessian filtering
                     (default: 2.0).
   :type sigma_min: float, optional
   :param sigma_max: Maximum scale for multi-scale Hessian filtering
                     (default: 8.0).
   :type sigma_max: float, optional
   :param alpha: Alpha parameter for vesselness computation
                 (default: 0.5).
   :type alpha: float, optional
   :param beta: Beta parameter for vesselness computation
                (default: 0.5).
   :type beta: float, optional
   :param gamma: Gamma parameter for vesselness computation
                 (default: 5.0).
   :type gamma: float, optional
   :param morph_open_radius: Radius for the optional morphological
                             opening operation (default: 1). If set to 0, no morphological opening
                             will be applied.
   :type morph_open_radius: int, optional
   :param \*\*hessian_func_kwargs: Additional keyword arguments for the Hessian
                                   objectness function.

   :returns: *ants.core.ANTsImage* -- The vesselness-enhanced image.

   .. rubric:: Notes

   - Input image must be 3D; an assertion will fail if a non-3D image is provided.
   - The input image is normalized before processing to ensure robustness.
   - Morphological opening, if applied, uses a grayscale operation to refine
     the tubular structures.
   - The function has defaults for vesselness computation, but can be used to detect
     globular or plate-like structures as well.

   :raises AssertionError: If the input image is not 3D.

   Workflow:
       1. Normalize the input image to have values between 0 and 1.
       2. Apply Hessian-based objectness filtering using the provided parameters
          (`sigma_min`, `sigma_max`, `alpha`, `beta`, `gamma`).
       3. Perform a morphological opening operation with the specified radius
          (`morph_open_radius`), if applicable.
       4. Return the computed vesselness image.