grid_skeleton

crispy.grid_ridge.grid_skeleton(coord, refdata, coord_in_xfirst=False, start_index=1)

Map CRISPy skeleton coordinates onto a reference image grid.

Takes CRISPy ridge coordinates and grids them onto a binary mask with the same shape as a reference image. The resulting mask highlights the skeletonized structure aligned to the grid.

Parameters:

• coord (ndarray) – Coordinates of the ridge points, shape (n, D), where n is the number of points and D is the dimensionality (2D or 3D).

• refdata (ndarray) – Reference image array defining the grid dimensions.

• coord_in_xfirst (bool, optional, default=False) – If True, assumes the input coordinates are ordered with x as the first axis. If False, assumes z is the first axis for 3D data or y for 2D data.

• start_index (int, optional, default=1) – Starting index for mapping the skeleton coordinates to the reference grid.

Returns:

mask – Binary mask with the same shape as refdata, where skeletonized points are set to 1 and all other points are 0.

Return type:

ndarray

Notes

• The coordinates are rounded to the nearest integer and adjusted for the starting index before mapping onto the reference grid.

• This function supports both 2D and 3D data.

Examples

Map 3D ridge coordinates onto a reference image grid:

>>> import numpy as np
>>> from crispy import grid_ridge
>>> coords = np.array([[0, 0, 0], [1, 1, 1], [10, 10, 10]])
>>> ref_image = np.zeros((20, 20, 20))  # Reference image
>>> mask = grid_ridge.grid_skeleton(coords, ref_image)
>>> print(mask.shape)
(20, 20, 20)