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Find connected components in binary image


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



CC = bwconncomp(BW) returns the connected components CC found in the binary image BW. 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) returns the connected components where conn specifies the desired connectivity for the connected components.


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Create a small sample 3-D array.

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]);

Find the connected components in the array.

CC = bwconncomp(BW)
CC = struct with fields:
    Connectivity: 26
       ImageSize: [3 3 3]
      NumObjects: 2
    PixelIdxList: {[5x1 double]  [3x1 double]}

Calculate centroids of the objects in the array.

S = regionprops(CC,'Centroid')
S = 2x1 struct array with fields:

Read image into the workspace and display it.

BW = imread('text.png');

Find the number of connected components in the image.

CC = bwconncomp(BW)
CC = struct with fields:
    Connectivity: 8
       ImageSize: [256 256]
      NumObjects: 88
    PixelIdxList: {1x88 cell}

Determine which is the largest component in the image and erase it (set all the pixels to 0).

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

Display the image, noting that the largest component happens to be the two consecutive f's in the word different.


Input Arguments

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Input binary image, specified as a real, nonsparse, numeric or logical array of any dimension.

Example: BW = imread('text.png'); CC = bwconncomp(BW);

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

Connectivity for the connected components, specified as one of the following scalar values.



Two-dimensional connectivities


4-connected neighborhood


8-connected neighborhood

Three-dimensional connectivities


6-connected neighborhood


18-connected neighborhood


26-connected neighborhood

To calculate the default connectivity for higher dimensions, bwconncomp uses conndef(ndims(BW),'maximal').

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.

Example: BW = imread('text.png'); CC = bwconncomp(BW,4);

Data Types: double | logical

Output Arguments

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Connected components, returned as a structure with four fields.

ConnectivityConnectivity of the connected components (objects)
ImageSizeSize of BW
NumObjectsNumber of connected components (objects) in BW
PixelIdxList1-by-NumObjects cell array where the k-th element in the cell array is a vector containing the linear indices of the pixels in the k-th object.


  • 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.

    FunctionInput DimensionOutput FormMemory UseConnectivity
    bwlabel2-DLabel matrix with double-precisionHigh4 or 8
    bwlabelnN-DDouble-precision label matrixHighAny
    bwconncompN-DCC structLowAny
  • 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.


The basic steps in finding the connected components are:

  1. Search for the next unlabeled pixel, p.

  2. Use a flood-fill algorithm to label all the pixels in the connected component containing p.

  3. Repeat steps 1 and 2 until all the pixels are labelled.

Extended Capabilities

Introduced in R2009a

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