The distance transform provides a metric or measure of the separation of points in the
bwdist function calculates the distance between each pixel
that is set to
0) and the nearest nonzero
pixel for binary images.
bwdist function supports several distance metrics, listed in
the following table.
The Euclidean distance is the straight-line distance between two pixels.
The city block distance metric measures the path between the pixels based on a 4-connected neighborhood. Pixels whose edges touch are 1 unit apart; pixels diagonally touching are 2 units apart.
The chessboard distance metric measures the path between the pixels based on an 8-connected neighborhood. Pixels whose edges or corners touch are 1 unit apart.
The quasi-Euclidean metric measures the total Euclidean distance along a set of horizontal, vertical, and diagonal line segments.
This example creates a binary image containing two intersecting circular objects.
center1 = -10; 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; figure, imshow(bw), title('bw')
To compute the distance transform of the complement of the binary image, use the
bwdist function. In the image of the distance transform, note how
the centers of the two circular areas are white.
D = bwdist(~bw); figure, imshow(D,), title('Distance transform of ~bw')