This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

bwskel

Reduce all objects to lines in 2-D binary image or 3-D binary volume

Syntax

B = bwskel(A)
B = bwskel(V)
B = bwskel(___,'MinBranchLength',N)

Description

example

B = bwskel(A) reduces all objects in the 2-D binary image A to 1-pixel wide curved lines, without changing the essential structure of the image. This process, called skeletonization, extracts the centerline while preserving the topology and Euler number of the objects.

example

B = bwskel(V) returns the skeleton of a 3-D binary volume.

example

B = bwskel(___,'MinBranchLength',N) specifies the minimum branch length N of the skeleton. bwskel removes (prunes) all branches shorter than the specified length. bwskel calculates the length as the number of pixels in a branch using 8-connectivity for 2-D and 26-connectivity for 3-D.

Examples

collapse all

Read a 2-D grayscale image into the workspace. Display the image. Objects of interest are dark threads against a light background.

I = imread('threads.png');
imshow(I)

Skeletonization requires a binary image in which foreground pixels are 1 (white) and the background is 0 (black). To make the original image suitable for skeletonization, take the complement of the image so that the objects are light and the background is dark. Then, binarize the result.

Icomplement = imcomplement(I);
BW = imbinarize(Icomplement);
imshow(BW)

Perform skeletonization of the binary image using bwskel.

out = bwskel(BW);

Display the skeleton over the original image by using the labeloverlay function. The skeleton appears as a 1-pixel wide cyan line over the dark threads.

imshow(labeloverlay(I,out,'Transparency',0))

Prune small spurs that appear on the skeleton and view the result. One short branch is pruned from a thread near the center of the image.

out2 = bwskel(BW,'MinBranchLength',15);
imshow(labeloverlay(I,out2,'Transparency',0))

Read a binary image into the workspace.

BW1 = imread('circbw.tif');

Skeletonize objects in the image using the bwskel function.

BW2 = bwskel(BW1);

View the original image and the skeletonized image side by side.

montage({BW1,BW2},'BackgroundColor','blue','BorderSize',5)

Load a volumetric data set into the workspace. The name of the data set is spiralVol. Display the volume using volshow.

load spiralVol.mat;
volshow(spiralVol);

Convert the spiralVol data set to a binary format which is required by the bwskel function.

spiralVolLogical = imbinarize(spiralVol);

Skeletonize the spiral shape in the data set. Display the skeletonized volume with volshow.

spiralVolSkel = bwskel(spiralVolLogical);

.

Input Arguments

collapse all

Binary image, specified as a 2-D logical array.

Data Types: logical

3-D binary volume, specified as a 3-D logical array.

Data Types: logical

Minimum branch length, specified as a nonnegative integer. bwskel prunes branches shorter than N. By default, bwskel does not prune branches.

Output Arguments

collapse all

Skeletonized image or volume, returned as a 2-D or 3-D logical array of the same size as the input image or volume.

Tips

  • While both bwskel and bwmorph can skeletonize 2-D images, you might get different results using bwmorph than when using bwskel. Because they use different algorithms, the bwskel function uses 4-connectivity with 2-D images; bwmorph uses 8-connectivity.

  • bwskel assumes that foreground objects in the binary image are white (logical true). If your image has a white background and black objects, then use the complement of your image as the input to bwskel. You can compute the complement using imcomplement.

Algorithms

  • The bwskel function uses the medial axis transform.

References

[1] Ta-Chih Lee, Rangasami L. Kashyap and Chong-Nam Chu. Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994.

[2] Kerschnitzki, M, Kollmannsberger, P, Burghammer, M. et al. Architecture of the osteocyte network correlates with bone material quality. Journal of Bone and Mineral Research, 28(8):1837-1845, 2013.

Introduced in R2018a