Hi,
To isolate the bright regions within contours in cardiac MRI images, you can use a technique called fuzzy c-means clustering to determine membership thresholds. To achieve the aim, follow the given below steps:
- Use fuzzy c-means clustering to segment the image into different regions based on intensity.
- Use the membership values from the fuzzy c-means clustering to create a binary mask that isolates the bright regions.
Sample Code with above consideration is given below:
% Read the cardiac MRI image
image = imread('cardiac_mri.jpg'); % Replace with your image file
if size(image, 3) == 3
image = rgb2gray(image); % Convert to grayscale if necessary
end
% Enhance the contrast of the image
image = imadjust(image);
% Reshape the image for clustering
imageData = double(image(:));
% Apply fuzzy c-means clustering
numClusters = 3; % You can adjust the number of clusters
[centers, U] = fcm(imageData, numClusters);
% Find the cluster with the highest intensity
[~, maxClusterIdx] = max(centers);
% Get membership values for the brightest cluster
membershipValues = U(maxClusterIdx, :);
% Reshape membership values to the original image size
membershipImage = reshape(membershipValues, size(image));
% Threshold the membership image to create a binary mask
threshold = 0.7; % Adjust the threshold as needed
binaryMask = membershipImage > threshold;
% Postprocess the binary mask
binaryMask = imfill(binaryMask, 'holes'); % Fill holes
binaryMask = bwareaopen(binaryMask, 50); % Remove small objects
% Display the results
figure;
subplot(1, 3, 1), imshow(image), title('Original Image');
subplot(1, 3, 2), imshow(membershipImage, []), title('Membership Image');
subplot(1, 3, 3), imshow(binaryMask), title('Isolated Bright Region');
For given input image and above consideration the results are:
For better understanding of Fuzzy C-Means Clustering refer to the following documentation:
- web(fullfile(docroot, "help/fuzzy/fcm.html"))
Hope that helps!
