Main Content

watershed

Watershed transform

collapse all in page

Syntax

L = watershed(A)
L = watershed(A,conn)

Description

The watershed transform finds "catchment basins" or "watershed ridge lines" in an image by treating it as a surface where light pixels represent high elevations and dark pixels represent low elevations. The watershed transform can be used to segment contiguous regions of interest into distinct objects.

example

L = watershed(A) returns a label matrix L that identifies the watershed regions of the input matrix A.

L = watershed(A,conn) specifies the connectivity to be used in the watershed computation.

Examples

collapse all

Open Live Script

Create a binary image containing two overlapping circular objects. Display the image.

center1 = -40;
center2 = -center1;
dist = sqrt(2*(2*center1)^2);
radius = dist/2 * 1.4;
lims = [floor(center1-1.2*radius) ceil(center2+1.2*radius)];
[x,y] = meshgrid(lims(1):lims(2));
bw1 = sqrt((x-center1).^2 + (y-center1).^2) <= radius;
bw2 = sqrt((x-center2).^2 + (y-center2).^2) <= radius;
bw = bw1 | bw2;
imshow(bw)
title('Binary Image with Overlapping Objects')

Figure contains an axes object. The axes object with title Binary Image with Overlapping Objects contains an object of type image.

Calculate the distance transform of the complement of the binary image. The value of each pixel in the output image is the distance between that pixel and the nearest nonzero pixel of bw.

D = bwdist(~bw);
imshow(D,[])
title('Distance Transform of Binary Image')

Figure contains an axes object. The axes object with title Distance Transform of Binary Image contains an object of type image.

Take the complement of the distance transformed image so that light pixels represent high elevations and dark pixels represent low elevations for the watershed transform.

D = -D;
imshow(D,[])
title('Complement of Distance Transform')

Figure contains an axes object. The axes object with title Complement of Distance Transform contains an object of type image.

Calculate the watershed transform. Set pixels that are outside the ROI to 0.

L = watershed(D);
L(~bw) = 0;

Display the resulting label matrix as an RGB image.

rgb = label2rgb(L,'jet',[.5 .5 .5]);
imshow(rgb)
title('Watershed Transform')

Figure contains an axes object. The axes object with title Watershed Transform contains an object of type image.

Open Live Script

Make a 3-D binary image containing two overlapping spheres. 

center1 = -10;
center2 = -center1;
dist = sqrt(3*(2*center1)^2);
radius = dist/2 * 1.4;
lims = [floor(center1-1.2*radius) ceil(center2+1.2*radius)];
[x,y,z] = meshgrid(lims(1):lims(2));
bw1 = sqrt((x-center1).^2 + (y-center1).^2 + ...
           (z-center1).^2) <= radius;
bw2 = sqrt((x-center2).^2 + (y-center2).^2 + ...
           (z-center2).^2) <= radius;
bw = bw1 | bw2;
figure, isosurface(x,y,z,bw,0.5), axis equal, title('BW')
xlabel x, ylabel y, zlabel z
xlim(lims), ylim(lims), zlim(lims)
view(3), camlight, lighting gouraud

Figure contains an axes object. The axes object with title BW contains an object of type patch.

Compute the distance transform.

D = bwdist(~bw);
figure, isosurface(x,y,z,D,radius/2), axis equal
title('Isosurface of distance transform')
xlabel x, ylabel y, zlabel z
xlim(lims), ylim(lims), zlim(lims)
view(3), camlight, lighting gouraud

Figure contains an axes object. The axes object with title Isosurface of distance transform contains an object of type patch.

Complement the distance transform, force nonobject pixels to be Inf, and then compute the watershed transform.

D = -D;
D(~bw) = Inf;
L = watershed(D);
L(~bw) = 0;
figure
isosurface(x,y,z,L==1,0.5)
isosurface(x,y,z,L==2,0.5)
axis equal
title('Segmented objects')
xlabel x, ylabel y, zlabel z
xlim(lims), ylim(lims), zlim(lims)
view(3), camlight, lighting gouraud

Figure contains an axes object. The axes object with title Segmented objects contains 2 objects of type patch.

Input Arguments

collapse all

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

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

Pixel connectivity, specified as one of the values in this table. The default connectivity is 8 for 2-D images, and 26 for 3-D images.

Value

Meaning

Two-Dimensional Connectivities

4

Pixels are connected if their edges touch. The neighborhood of a pixel are the adjacent pixels in the horizontal or vertical direction.

3-by-3 pixel neighborhood with four pixels connected to the center pixel

Current pixel is shown in gray.

8

Pixels are connected if their edges or corners touch. The neighborhood of a pixel are the adjacent pixels in the horizontal, vertical, or diagonal direction.

3-by-3 pixel neighborhood with 8 pixels connected to the center pixel

Current pixel is shown in gray.

Three-Dimensional Connectivities

6

Pixels are connected if their faces touch. The neighborhood of a pixel are the adjacent pixels in:

  • One of these directions: in, out, left, right, up, and down

3-by-3-by-3 pixel neighborhood with 6 pixels connected to the faces of the center pixel

Current pixel is shown in gray.

18

Pixels are connected if their faces or edges touch. The neighborhood of a pixel are the adjacent pixels in:

  • One of these directions: in, out, left, right, up, and down

  • A combination of two directions, such as right-down or in-up

3-by-3-by-3 pixel neighborhood with 6 pixels connected to the faces and 12 pixels connected to the edges of the center pixel

Current pixel is center of cube.

26

Pixels are connected if their faces, edges, or corners touch. The neighborhood of a pixel are the adjacent pixels in:

  • One of these directions: in, out, left, right, up, and down

  • A combination of two directions, such as right-down or in-up

  • A combination of three directions, such as in-right-up or in-left-down

3-by-3-by-3 pixel neighborhood with 6 pixels connected to the faces, 12 pixels connected to the edges, and 8 pixels connected to the corners of the center pixel

Current pixel is center of cube.

For higher dimensions, watershed uses the default value conndef(ndims(A),'maximal').

Connectivity can also be defined in a more general way for any dimension by specifying a 3-by-3-by- ... -by-3 matrix of 0s and 1s. The 1-valued elements define neighborhood locations relative to the center element of conn. Note that conn must be symmetric about its center element. See Specifying Custom Connectivities for more information.

Note

If you specify a nondefault connectivity, pixels on the edge of the image might not be considered to be border pixels. For example, if conn = [0 0 0; 1 1 1; 0 0 0], elements on the first and last row are not considered to be border pixels because, according to that connectivity definition, they are not connected to the region outside the image.

Data Types: double | logical

Output Arguments

collapse all

Label matrix, specified as a numeric array of nonnegative integers. The elements labeled 0 do not belong to a unique watershed region. The elements labeled 1 belong to the first watershed region, the elements labeled 2 belong to the second watershed region, and so on.

Tips

  • The watershed transform algorithm used by this function changed in version 5.4 (R2007a) of the Image Processing Toolbox™ software. The previous algorithm occasionally produced labeled watershed basins that were not contiguous. If you need to obtain the same results as the previous algorithm, use the function watershed_old.

  • To prevent oversegmentation, remove shallow minima from the image by using the imhmin function before you use the watershed function.

Algorithms

watershed uses the Fernand Meyer algorithm [1].

References

[1] Meyer, Fernand, "Topographic distance and watershed lines,” Signal Processing , Vol. 38, July 1994, pp. 113-125.

Extended Capabilities

Version History

Introduced before R2006a

expand all

See Also

| | | |