Label connected components in binary image
L = bwlabeln(BW)
[L, NUM] = bwlabeln(BW)
[L, NUM] = bwlabeln(BW, conn)
L = bwlabeln(BW) returns a label matrix, L, containing labels for the connected components in BW. The input image BW can have any dimension; L is the same size as BW. The elements of L are integer values greater than or equal to 0. The pixels labeled 0 are the background. The pixels labeled 1 make up one object; the pixels labeled 2 make up a second object; and so on. The default connectivity is 8 for two dimensions, 26 for three dimensions, and conndef(ndims(BW), 'maximal') for higher dimensions.
Connectivity can also be defined in a more general way for any dimension by using for conn a 3-by-3-by- ...-by-3 matrix of 0s and 1s. The 1-valued elements define neighborhood locations relative to the central element of conn. Note that conn must be symmetric about its central element.
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 older functions.
|Function||Input Dimension||Output Form||Memory Use||Connectivity|
|bwlabel||2-D||Double-precision label matrix||High||4 or 8|
|bwlabeln||N-D||Double-precision label matrix||High||Any|
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]) bwlabeln(BW) ans(:,:,1) = 1 1 0 0 0 0 2 0 0 ans(:,:,2) = 0 1 0 0 0 0 0 2 0 ans(:,:,3) = 0 1 1 0 0 0 0 0 2
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 a more memory-efficient data type (e.g., uint8 versus double), use the labelmatrix function on the output of bwconncomp:
C = bwconncomp(BW); L = labelmatrix(CC);
CC = bwconncomp(BW, conn); S = regionprops(CC);