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bundles / skimage latest / skimage / morphology / _skeletonize / medial_axis

function

skimage.morphology._skeletonize:medial_axis

source: /dev/scikit-image/src/skimage/morphology/_skeletonize.py :360

Signature

def   medial_axis ( image ,  mask = None ,  return_distance = False ,  * ,  rng = None )

Summary

Compute the medial axis transform of a binary image.

Parameters

image : binary ndarray, shape (M, N)

The image of the shape to skeletonize. If this input isn't already a binary image, it gets converted into one: In this case, zero values are considered background (False), nonzero values are considered foreground (True).

mask : binary ndarray, shape (M, N), optional

If a mask is given, only those elements in image with a true value in mask are used for computing the medial axis.

return_distance : bool, optional

If true, the distance transform is returned as well as the skeleton.

rng : {`numpy.random.Generator`, int}, optional

Pseudo-random number generator. By default, a PCG64 generator is used (see numpy.random.default_rng). If rng is an int, it is used to seed the generator.

The PRNG determines the order in which pixels are processed for tiebreaking.

Returns

out : ndarray of bools

Medial axis transform of the image

dist : ndarray of ints, optional

Distance transform of the image (only returned if return_distance is True)

Notes

This algorithm computes the medial axis transform of an image as the ridges of its distance transform.

The different steps of the algorithm are as follows

  • A lookup table is used, that assigns 0 or 1 to each configuration of the 3x3 binary square, whether the central pixel should be removed or kept. We want a point to be removed if it has more than one neighbor and if removing it does not change the number of connected components.

  • The distance transform to the background is computed, as well as the cornerness of the pixel.

  • The foreground (value of 1) points are ordered by the distance transform, then the cornerness.

  • A cython function is called to reduce the image to its skeleton. It processes pixels in the order determined at the previous step, and removes or maintains a pixel according to the lookup table. Because of the ordering, it is possible to process all pixels in only one pass.

Examples

square = np.zeros((7, 7), dtype=bool)
square[1:-1, 2:-2] = 1
square.view(np.uint8)
medial_axis(square).view(np.uint8)
✓

See also

skeletonize
thin

Aliases

  • skimage.morphology.medial_axis

Referenced by

This package