I was looking at the image below on a web page recently and I decided to extract the locations of all the dots. I thought the procedure might make a nice little end-of-the-week how-to post.
% Did you know that imread can read directly from a URL? url = 'http://cgm.cs.mcgill.ca/~godfried/teaching/projects97/belair/example1.gif'; [X, map] = imread(url); imshow(X, map)
First question to answer: What is the index of the color used for the dots? One easy way to go is to use imtool.
Here's a screenshot:
As you can see, when I placed the mouse pointer over the center of one of the dots, the pixel info display at the lower left showed that the index value was 1, and the corresponding colormap color was [1.00, 0.78, 0.00].
Now that we know the index value, we can easily make a binary image of just the interior of the dots.
bw = X == 1; imshow(bw)
Now we can compute the centroids of the dots.
s = regionprops(bw, 'Centroid')
s =
122x1 struct array with fields:
Centroid
The line above is a new syntax for regionprops that we introduced earlier this year in R2009a. Previously, you always had to compute a label matrix first using bwlabel and then pass that label matrix to regionprops. Now regionprops can do this computation directly on the binary image. Since the label matrix is not computed, this new syntax uses less memory and usually runs faster than the old version.
For those of you using an older version, do this instead:
L = bwlabel(bw); s = regionprops(L, 'Centroid');
To show the results, display the image and then superimpose the centroid locations.
imshow(X, map) hold on for k = 1:numel(s) centroid_k = s(k).Centroid; plot(centroid_k(1), centroid_k(2), 'b.'); end hold off
Hmm. It looks we got only one dot at some of those locations. Use the zoom button on the Figure Window toolbar to zoom in. Or you can do it noninteractively:
axis([50 100 0 50])
Yep. For overlapping circles, we are only getting one location. The reason is that the interiors of the overlapping circles are 8-connected to each other, so they are regarded by regionprops as single regions. To get two locations for these overlapping circles, we have to label the regions using 4-connectivity instead of 8-connectivity.
To do this we have to compute the connected components in a separate step and then call regionprops.
cc = bwconncomp(bw, 4);
s = regionprops(cc, 'Centroid');bwconncomp is also new to R2009a. Users of R2008b and earlier can do:
L = bwlabel(bw, 4); s = regionprops(L, 'Centroid');
imshow(X, map) hold on for k = 1:numel(s) centroid_k = s(k).Centroid; plot(centroid_k(1), centroid_k(2), 'b.'); end hold off axis([50 100 0 50])
Much better.
This little example showed several useful things:
- Reading an image directly from a URL
- Using imtool to inspect individual pixel values
- Using the new, more efficient syntax of regionprops
- Using the new bwconncomp
- Superimposing plots on an image
Have a happy weekend!
Get
the MATLAB code
Published with MATLAB® 7.9
Hello Steve,
i tried to apply your example code to seperating two centroids of the overlapping lines in the binary image, it did not work.
is your code limited to circular objects? how can i modify it to find out two/three centroids of two/three overlapping lines.
thanks
DP
DP—No, there’s nothing specific to circles in my code. I don’t really understand your question; what is “two overlapping lines”?
like two overlapping circles in your case, i have two or three overlapping line segments in my binary image.
when i apply the following,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
L = bwlabel(bw, 4);
s = regionprops(L, ‘Centroid’);imshow(X, map)
hold on
for k = 1:numel(s)
centroid_k = s(k).Centroid;
plot(centroid_k(1), centroid_k(2), ‘b.’);
end
hold off
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
i still got only one centroid for the two line segments which are overlapped.
i wanted to have two centroides for the two overlapping line segments.
thanks
DP
DP—Then your two regions are connected to each other and are really just one region.
thats right. is there any way to seperate these line segments and find individual centroid?
thanks.
DP
DP—You could try dilating the lines dividing the regions to make them thicker and fill in gaps.
Hi Steve,
thanks for your email.
i could not seperate those connected objects. Instead i am trying to compare similarity measured between two set of images.
i heared about dice similarity coefficients,to compare two similar images, do you have any matlab functions to compute dice similarity coefficients between two images.
thanks.
DP
DP—Sorry, but I’ve never heard of dice similarity coefficients.
Hi Steve,
Dice similarity coefficient is defined to find the overlapped regions between two objects in an image.
For example, DSC = 2.0(A intersection B)/(A+B), where A and B are two objects in an image. if A = B, DSC = 1.0
if A is not equal to B, DSC = 0. in general DSC lies between 0 to 1.
so, first of all i am trying to find intersection between two image objects? do you have any suggessition how to compute intersection?
thanks.
DP
DP—
Thanks Steve. it did work. now can easily calculate DSC.