Segment connected regions in binary image

Question

Adenine Koo 2024-9-5

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评论： Adenine Koo 2024-9-10

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!

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Adenine Koo 2024-9-5

This is the link to the raw tif image: 5KQ-005_RGB_488.tif

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Answer 1

Image Analyst 2024-9-6

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https://ww2.mathworks.cn/matlabcentral/answers/2150479-segment-connected-regions-in-binary-image#answer_1511759

See these links:

Watershed transform question from tech support » Watershed transform question from tech support

Marker-Controlled Watershed Segmentation - MATLAB & Simulink Example

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

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Adenine Koo 2024-9-7

在 MATLAB Online 中打开

These are the intermediate results before bwconvhull

thisImage = imread(thisFile);
% Plot the original image
subplot(2, 3, 1);
imshow(thisImage)
titleLabel = sprintf("Original Image #%d", ii); % title label
title(titleLabel, 'FontSize', titlefontSize);
% Convert the RGB values to HSV values
hsv = rgb2hsv((thisImage));
% Extract the brightness value from the third dimension of the array
brightnessValue = hsv(:, :, 3);
% Plot the brightness value of the image
subplot(2, 3, 2);
imshow(brightnessValue, [])
title('Brightness Value', 'FontSize', titlefontSize);
% Convert to binary image
BW = imbinarize(brightnessValue, 'adaptive', 'Sensitivity', 0.05);
subplot(2, 3, 3);
imshow(BW, [])
title('Value Mask', 'FontSize', titlefontSize);
% Remove all small objects/connected components
P = 20; % the number of connected pixels
BW2 = bwareaopen(BW, P); % default: 8-connected
% Fill holes in the binary image --> area opening
BW3 = imfill(BW2, 'holes'); % default: 4-connected
% Plot the finalised value mask
subplot(2, 3, 4);
imshow(BW3, [])
title('Filled Value Mask', 'FontSize', titlefontSize);
% Donut segmentation by watershed to resolve connected donuts/touching
% blobs
% Checkout https://blogs.mathworks.com/steve/2013/11/19/watershed-transform-question-from-tech-support/
% Distance transform
D = -bwdist(~BW3);
% Minima imposition
% Create a new image (mask) whose catchment basins/local minima are
% the donuts for segmentation by filtering out tiny local minima
mask = imextendedmin(D, 2);
subplot(2, 3, 5);
% Superimpose the mask on the original image
imshowpair(BW3, mask, "blend")
title('Minima Imposed', 'FontSize', titlefontSize);
% Modify the distance transform so it only has minima at the desired
% locations
D2 = imimposemin(D, mask);
% The watershed ridge lines (in white) correspond to Ld == 0
% Set these ridge lines to 0 to recreate the donut boundaries/segments
Ld2 = watershed(D2);
BW4 = BW3;
BW4(Ld2 == 0) = 0;
subplot(2, 3, 6);
imshow(BW4, [])
title("Watershed Segment", 'FontSize', titlefontSize);

Image Analyst 2024-9-10

Is there a minimum size? So is there a size for which you only want blobs larger than that size? Like, can we ignore small blobs? Or blobs with no holes?

Adenine Koo 2024-9-10

Probably no, they can be of a variety of sizes, but you are right, I guess I will have to sacrifice the very small one just to eliminate the background glitches too. I do want to include bigger blobs without holes.

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Answer 2

Shivansh 2024-9-6

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编辑：Shivansh 2024-9-6

在 MATLAB Online 中打开

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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Adenine Koo 2024-9-6

This is roughly how I do it in the beginning which does not work well, but the DL based approach is something worth looking at! Thanks!

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Segment connected regions in binary image

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