# Segment connected regions in binary image

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Adenine Koo
on 5 Sep 2024 at 20:10

Commented: Adenine Koo
on 10 Sep 2024 at 15:27

Hi MATLAB community

I want to extract the area of these spherical structures from my binary image but there are circles which are too close to each other and hence became connected. I tried to use watershed to separate them but it does not work good enough. Also, I have half-opened circles (the crescent like structures) which bwareaopen is not helpful to close them too. Any suggestion is welcome!

### Accepted Answer

Image Analyst
on 6 Sep 2024 at 4:37

See these links:

Then to close the bays in the blobs, I would not use imclose. I would use bwconvhull for obvious reasons.

##### 10 Comments

Image Analyst
on 10 Sep 2024 at 3:36

### More Answers (1)

Shivansh
on 6 Sep 2024 at 4:31

Edited: Shivansh
on 6 Sep 2024 at 4:34

Hi Adenine!

It seems like you are having issues in identifying the circles present in your image.

You can start by applying a Gaussian blur to reduce noise, followed by adaptive thresholding for more effective segmentation.

You can then use "imclose" to fill gaps in half-opened circles. You can also try to compute the distance transform with "bwdist" and apply a marker-controlled watershed technique. If the spherical structures are circular, the Hough Transform via "imfindcircles" can be quite useful for detecting and distinguishing these shapes.

You can refer to the below script for reference:

I = imread('5KQ-005_RGB_488.tif');

if size(I, 3) == 3

I = rgb2gray(I);

end

% Apply Gaussian blur

I_blur = imgaussfilt(I, 2);

% Convert to binary image

bw = imbinarize(I_blur, 'adaptive', 'Sensitivity', 0.5);

% Apply morphological closing to fill gaps

se = strel('disk', 5);

bw_closed = imclose(bw, se);

% Compute the distance transform

D = -bwdist(~bw_closed);

% Set background pixels to -Inf

D(~bw_closed) = -Inf;

% Apply watershed transform

L = watershed(D);

% Display the labeled image

imshow(label2rgb(L, 'jet', 'w', 'shuffle'));

You can experiment with different combinations of techniques and parameters to analyze the results for your case.

If you don't get the desired results, you can also look towards deep learning related approaches.

You can refer to the following documentation link for DL related approaches: https://www.mathworks.com/help/images/deep-learning.html.

I hope this helps in resolving your issue.

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