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bwconncomp - Find connected components in binary image

Syntax

CC = bwconncomp(BW) CC = bwconncomp(BW,conn)

Description

CC = bwconncomp(BW) returns the connected components CC found in BW. The binary image BW can have any dimension. CC is a structure with four fields.

Field	Description
`Connectivity`	Connectivity of the connected components (objects)
`ImageSize`	Size of `BW`
`NumObjects`	Number of connected components (objects) in `BW`
`PixelIdxList`	`1-by-NumObjects` cell array where the kth element in the cell array is a vector containing the linear indices of the pixels in the kth object.

bwconncomp uses a default connectivity of 8 for two dimensions, 26 for three dimensions, and conndef(ndims(BW),'maximal') for higher dimensions.

CC = bwconncomp(BW,conn) specifies the desired connectivity for the connected components. conn can have the following scalar values.

Value	Meaning
Two-dimensional connectivities
4	4-connected neighborhood
8	8-connected neighborhood
Three-dimensional connectivities
6	6-connected neighborhood
18	18-connected neighborhood
26	26-connected neighborhood

Connectivity can be defined in a more general way for any dimension using a 3-by-3-by- ... -by-3 matrix of 0s and 1s. conn must be symmetric about its center element. The 1-valued elements define neighborhood locations relative to conn.

Remarks

The functions bwlabel, bwlabeln, and bwconncomp all compute connected components for binary images. bwconncomp replaces the use of bwlabel and bwlabeln. It uses significantly less memory and is sometimes faster than the other functions.

Function	Input Dimension	Output Form	Memory Use	Connectivity
`bwlabel`	2-D	Label matrix with double-precision	High	4 or 8
`bwlabeln`	N-D	Double-precision label matrix	High	Any
`bwconncomp`	N-D	`CC` struct	Low	Any

To extract features from a binary image using regionprops with default connectivity, just pass BW directly into regionprops (i.e., regionprops(BW)).

To compute a label matrix having more memory-efficient data type (e.g., uint8 versus double), use the labelmatrix function on the output of bwconncomp. See the documentation for each function for more information.

Class Support

BW can be a logical or numeric array of any dimension, and it must be real and nonsparse. CC is a structure.

Examples

Example 1

Calculate the centroids of the 3-D objects.

BW = cat(3, [1 1 0; 0 0 0; 1 0 0],...
            [0 1 0; 0 0 0; 0 1 0],...
            [0 1 1; 0 0 0; 0 0 1]);

CC = bwconncomp(BW);
S = regionprops(CC,'Centroid');

Example 2

Erase the largest letter from the image.

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

CC = bwconncomp(BW);
numPixels = cellfun(@numel,CC.PixelIdxList);
[biggest,idx] = max(numPixels);
BW(CC.PixelIdxList{idx}) = 0;
 
figure, imshow(BW);