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bwmorph

Morphological operations on binary images

Syntax

BW2 = bwmorph(BW,operation)
BW2 = bwmorph(BW,operation,n)
gpuarrayBW2 = bwmorph(gpuarrayBW,___)

Description

BW2 = bwmorph(BW,operation) applies a specific morphological operation to the binary image BW.

BW2 = bwmorph(BW,operation,n) applies the operation n times. n can be Inf, in which case the operation is repeated until the image no longer changes.

gpuarrayBW2 = bwmorph(gpuarrayBW,___) performs the morphological operation on a GPU. The input image and output image are gpuArrays. This syntax requires the Parallel Computing Toolbox™.

Supported Morphological Operations

operation is a string that can have one of the values listed below.

Operation

Description

'bothat'

Performs the morphological "bottom hat" operation, returning the image minus the morphological closing of the image (dilation followed by erosion).

'branchpoints'

Find branch points of skeleton. For example:

0  0  1  0  0           0  0  0  0  0
0  0  1  0  0  becomes  0  0  0  0  0
1  1  1  1  1           0  0  1  0  0
0  0  1  0  0           0  0  0  0  0
0  0  1  0  0           0  0  0  0  0

Note: To find branch points, the image must be skeletonized. To create a skeletonized image, use bwmorph(BW,'skel').

'bridge'

Bridges unconnected pixels, that is, sets 0-valued pixels to 1 if they have two nonzero neighbors that are not connected. For example:

1  0  0           1  1  0 
1  0  1  becomes  1  1  1 
0  0  1           0  1  1

'clean'

Removes isolated pixels (individual 1s that are surrounded by 0s), such as the center pixel in this pattern.

0  0  0 
0  1  0 
0  0  0

'close'

Performs morphological closing (dilation followed by erosion).

'diag'

Uses diagonal fill to eliminate 8-connectivity of the background. For example:

0  1  0           0  1  0 
1  0  0  becomes  1  1  0 
0  0  0           0  0  0

'endpoints'

Finds end points of skeleton. For example:

1  0  0  0           1  0  0  0
0  1  0  0  becomes  0  0  0  0
0  0  1  0           0  0  1  0
0  0  0  0           0  0  0  0

Note: To find end points, the image must be skeletonized. To create a skeletonized image, use bwmorph(BW,'skel').

'fill'

Fills isolated interior pixels (individual 0s that are surrounded by 1s), such as the center pixel in this pattern.

1  1  1 
1  0  1 
1  1  1

'hbreak'

Removes H-connected pixels. For example:

1  1  1           1  1  1 
0  1  0  becomes  0  0  0 
1  1  1           1  1  1

'majority'

Sets a pixel to 1 if five or more pixels in its 3-by-3 neighborhood are 1s; otherwise, it sets the pixel to 0.

'open'

Performs morphological opening (erosion followed by dilation).

'remove'

Removes interior pixels. This option sets a pixel to 0 if all its 4-connected neighbors are 1, thus leaving only the boundary pixels on.

'shrink'

With n = Inf, shrinks objects to points. It removes pixels so that objects without holes shrink to a point, and objects with holes shrink to a connected ring halfway between each hole and the outer boundary. This option preserves the Euler number.

'skel'

With n = Inf, removes pixels on the boundaries of objects but does not allow objects to break apart. The pixels remaining make up the image skeleton. This option preserves the Euler number.

'spur'

Removes spur pixels. For example:

0  0  0  0           0  0  0  0
0  0  0  0           0  0  0  0
0  0  1  0  becomes  0  0  0  0
0  1  0  0           0  1  0  0
1  1  0  0           1  1  0  0

'thicken'

With n = Inf, thickens objects by adding pixels to the exterior of objects until doing so would result in previously unconnected objects being 8-connected. This option preserves the Euler number.

'thin'

With n = Inf, thins objects to lines. It removes pixels so that an object without holes shrinks to a minimally connected stroke, and an object with holes shrinks to a connected ring halfway between each hole and the outer boundary. This option preserves the Euler number. See Algorithms for more detail.

'tophat'

Performs morphological "top hat" operation, returning the image minus the morphological opening of the image (erosion followed by dilation).

Code Generation

bwmorph supports the generation of efficient, production-quality C/C++ code from MATLAB. The text string specifying the operation must be a constant and, for best results, specify an input image of class logical. To see a complete list of toolbox functions that support code generation, see List of Supported Functions with Usage Notes.

Class Support

The input image BW can be numeric or logical. It must be 2-D, real and nonsparse. The output image BW2 is of class logical.

The input gpuArray image gpuarrayBW can be numeric or logical, but must be 2–D, real, and nonsparse. The output gpuArray image gpuarrayBW2 is logical.

Examples

This example reads a binary image into the workspace and then performs several morphological operations on it.

BW = imread('circles.png');
imshow(BW);

Remove interior pixels to leave an outline of the shapes.

BW2 = bwmorph(BW,'remove');
figure, imshow(BW2)

Get the image skeleton.

BW3 = bwmorph(BW,'skel',Inf);
figure, imshow(BW3)

Execute the same code on a GPU.

% Read image into a gpuArray
BW1 = gpuArray(imread('circles.png'));
figure, imshow(BW1)

BW2 = bwmorph(BW1,'remove');
figure, imshow(BW2)

BW3 = bwmorph(BW1,'skel',Inf);
figure, imshow(BW3)

More About

expand all

Tips

  • To perform erosion or dilation using the structuring element ones(3), use imerode or imdilate.

Algorithms

When used with the 'thin' option, bwmorph uses the following algorithm (References [3]):

  1. In the first subiteration, delete pixel p if and only if the conditions G1, G2, and G3 are all satisfied.

  2. In the second subiteration, delete pixel p if and only if the conditions G1, G2, and are all satisfied.

Condition G1:

where

x1, x2, ..., x8 are the values of the eight neighbors of p, starting with the east neighbor and numbered in counter-clockwise order.

Condition G2:

where

Condition G3:

Condition G3':

The two subiterations together make up one iteration of the thinning algorithm. When the user specifies an infinite number of iterations (n=Inf), the iterations are repeated until the image stops changing. The conditions are all tested using applylut with precomputed lookup tables.

References

[1] Haralick, Robert M., and Linda G. Shapiro, Computer and Robot Vision, Vol. 1, Addison-Wesley, 1992.

[2] Kong, T. Yung and Azriel Rosenfeld, Topological Algorithms for Digital Image Processing, Elsevier Science, Inc., 1996.

[3] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning Methodologies-A Comprehensive Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 14, No. 9, September 1992, page 879, bottom of first column through top of second column.

[4] Pratt, William K., Digital Image Processing, John Wiley & Sons, Inc., 1991.

See Also

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