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Segment image into foreground and background using active contours (snakes) region growing technique


The active contours technique, also called snakes, is an iterative region-growing image segmentation algorithm. Using the active contour algorithm, you specify initial curves on an image and then use the activecontour function to evolve the curves towards object boundaries.


BW = activecontour(A,mask) segments the image A into foreground (object) and background regions using active contours.

The mask argument is a binary image that specifies the initial state of the active contour. The boundaries of the object regions (white) in mask define the initial contour position used for contour evolution to segment the image. The output image BW is a binary image where the foreground is white (logical true) and the background is black (logical false).

To obtain faster and more accurate segmentation results, specify an initial contour position that is close to the desired object boundaries.

BW = activecontour(A,mask,n) segments the image by evolving the contour for a maximum of n iterations.


BW = activecontour(A,mask,method) specifies the active contour method used for segmentation as either "Chan-Vese" or "edge". For RGB images, the method must be "Chan-Vese".

BW = activecontour(A,mask,n,method) segments the image by evolving the contour for a maximum of n iterations using the specified method.

BW = activecontour(___,Name,Value) specifies name-value arguments that control various aspects of the segmentation.


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Read and display a grayscale image.

I = imread('coins.png');
title('Original Image')

Specify an initial contour surrounding the objects of interest. Display the contour.

mask = zeros(size(I));
mask(25:end-25,25:end-25) = 1;
title('Initial Contour Location')

Segment the image by using the activecontour function. By default, the function evolves the segmentation through 100 iterations.

bw = activecontour(I,mask);

Display the result. After 100 iterations, objects are not fully segmented from the background because the original contour is not close to the object boundaries.

title('Segmented Image, 100 Iterations')

To continue evolving the segmentation, increase the number of iterations. After 300 iterations, objects are fully segmented from the background.

bw = activecontour(I,mask,300);
title('Segmented Image, 300 Iterations')

Read and display a grayscale image.

I = imread('toyobjects.png');

Draw an initial contour close to the object of interest by using the drawrectangle function. After drawing the contour, create a mask by using the createMask function.

r = drawrectangle;

mask = createMask(r);

Segment the image using the 'edge' method and 200 iterations.

bw = activecontour(I,mask,200,'edge');

Display the final contour over the original image in red.

hold on;

Display the result of the segmentation over the original image. The object in the foreground has a blue color.


Load 3-D volumetric image data, removing the singleton dimension.

D = load('mri.mat');
A  = squeeze(D.D);

Create 2-D mask for initial seed points.

seedLevel = 10;
seed = A(:,:,seedLevel) > 75;

Create an empty 3-D seed mask and put the seed points into it.

mask = zeros(size(A));
mask(:,:,seedLevel) = seed;

Perform the segmentation using active contours, specifying the seed mask.

bw = activecontour(A,mask,300);

Display the 3-D segmented image.

p = patch(isosurface(double(bw)));
p.FaceColor = 'red';
p.EdgeColor = 'none';
daspect([1 1 27/128]);
lighting phong

Input Arguments

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Image to segmented, specified as a 2-D numeric matrix or 3-D numeric array.

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

Initial contour at which the evolution of the segmentation begins, specified as a binary image of the same size as A. For 2-D and 3-D grayscale images, the size of mask must match the size of the image A. For color and multi-channel images, mask must be a 2-D logical array where the first two dimensions match the first two dimensions of the image A.

You can create a mask interactively by using ROI objects. For example, draw a polygonal ROI by using the drawpolygon function, then create a mask from the ROI by using the createMask function.

Data Types: logical

Maximum number of iterations to perform in evolution of the segmentation, specified as a positive integer. activecontour stops the evolution of the active contour when it reaches the maximum number of iterations. activecontour also stops the evolution if the contour position in the current iteration is the same as the contour position in one of the most recent five iterations.

If the initial contour position (specified by mask) is far from the object boundaries, specify larger values of n to achieve desired segmentation results.

Active contour method used for segmentation, specified as "Chan-Vese" or "edge". The Chan-Vese region-based energy model is described in [1]. The edge-based model, similar to geodesic active contours, is described in [2].

For RGB images, the method must be "Chan-Vese".

Data Types: char | string

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: SmoothFactor=1.5

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: "SmoothFactor",1.5

Degree of smoothness or regularity of the boundaries of the segmented regions, specified as a nonnegative number. Higher values produce smoother region boundaries but can also smooth out finer details. Lower values produce more irregularities (less smoothing) in the region boundaries but allow finer details to be captured. The default smoothness value is 0 for the "Chan-Vese" method and 1 for the "edge" method.

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

Tendency of the contour to grow outwards or shrink inwards, specified as a number. Positive values bias the contour to shrink inwards (contract). Negative values bias the contour to grow outwards (expand). This argument does not guarantee that the contour contracts or expands. It is possible that even with a positive value for this argument, the contour could actually expand. However, by specifying a bias, you slow the expansion when compared to an unbiased contour. Typical values for this argument are between -1 and 1. The default contraction bias is 0 for the "Chan-Vese" method and 0.3 for the "edge" method.

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

Output Arguments

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Segmented image, returned as a logical array of the same size as the input image A. The foreground is white (logical true) and the background is black (logical false).

Data Types: logical


  • activecontour uses the boundaries of the regions in mask as the initial state of the contour from where the evolution starts. Holes in the mask can cause unpredictable results. Use imfill to fill any holes in the regions in mask.

  • If a region touches the image borders, then activecontour removes a single-pixel layer from the region, before further processing, so that the region does not touch the image border.

  • To get faster and more accurate results, specify an initial contour position that is close to the desired object boundaries, especially for the "edge" method.

  • For the "edge" method, the active contour is naturally biased towards shrinking inwards (collapsing). In the absence of any image gradient, the active contour shrinks on its own. Conversely, with the "Chan-Vese" method, where the contour is unbiased, the contour is free to either shrink or expand based on the image features.

  • To achieve an accurate segmentation with the "edge" method, specify an initial contour that lies outside the boundaries of the object. The active contour with the "edge" method is biased to shrink, by default.

  • If object regions are of significantly different grayscale intensities, then the "Chan-Vese" method [1] might not segment all objects in the image. For example, if the image contains objects that are brighter than the background and some that are darker, the "Chan-Vese" method typically segments out either the dark or the bright objects only.


activecontour uses the Sparse-Field level-set method, similar to the method described in [3], for implementing active contour evolution.


[1] T. F. Chan, L. A. Vese, Active contours without edges. IEEE Transactions on Image Processing, Volume 10, Issue 2, pp. 266-277, 2001.

[2] V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours. International Journal of Computer Vision, Volume 22, Issue 1, pp. 61-79, 1997.

[3] R. T. Whitaker, A level-set approach to 3d reconstruction from range data. International Journal of Computer Vision, Volume 29, Issue 3, pp. 203-231, 1998.

Extended Capabilities

Version History

Introduced in R2013a

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