how to apply k means clustering algorithm for image segmentation in matlab and how to use kernel methods in that code

Hi, i don't know how to apply k means clustering algorithm to images for segmenting a portion. Any help is appreciated

[Hiro Yoshino]

Can you dumb down the question?

Where are you stuck?

[MINO GEORGE]

Hi,

I am working on ultrasound images and i wanted to segment a particular portion from the image. I would like to try different kernel methods like linear, quadratic, rbf etc. in k means clustering algorithm. I have more than 100 images and i am trying to apply k means clustering technique, but iam not able to understand the algorithm. Pls help me.

Answer 1

[Sanju]

Hi MINO GEORGE,

I understand that you are looking for a method to implement K means Clustering algorithm,

The “k-means clustering” algorithm is an unsupervised learning technique used to partition a dataset into ‘k’ clusters. In the context of image segmentation, each pixel in the image is treated as a data point, and the algorithm groups similar pixels together based on their feature values.

To apply the “k-means clustering” algorithm in MATLAB, you can use the “kmeans” function. Here's an example code you may refer to understand how to use the "kmeans" function for image segmentation,

% Load the ultrasound image
image = imread('ultrasound_image.png');

% Convert the image to grayscale
grayImage = rgb2gray(image);

% Reshape the image into a column vector
data = double(grayImage(:));

% Normalize the data
data = (data - min(data)) / (max(data) - min(data));

% Apply k-means clustering with different kernel methods
k = 2; % Number of clusters
maxIterations = 100; % Maximum number of iterations

% Linear kernel
[idx_linear, centroids_linear] = kmeans(data, k, 'Distance', 'sqeuclidean', 'MaxIter', maxIterations);

% RBF kernel
[idx_rbf, centroids_rbf] = kmeans(data, k, 'Distance', 'cityblock', 'MaxIter', maxIterations);

% Reshape the segmented image
segmented_linear = reshape(centroids_linear(idx_linear), size(grayImage));
segmented_rbf = reshape(centroids_rbf(idx_rbf), size(grayImage));

% Display the segmented images
figure;
subplot(1, 2, 1);
imshow(segmented_linear, []);
title('Linear Kernel');
subplot(1, 2, 2);
imshow(segmented_rbf, []);
title('RBF Kernel');

The linear kernel calculates similarity using squared Euclidean distance, assuming data is in a straight line. The RBF kernel measures similarity based on Euclidean distance, assuming data is distributed in a curve. These kernel methods allow the k-means clustering algorithm to handle different types of data distributions and capture complex patterns in the data.

You can experiment with different values of k and try different feature representations of the image (e.g., using colour information) to improve the segmentation results.

You can also refer to the below documentation links if required,

https://www.mathworks.com/help/matlab/ref/imread.html

https://www.mathworks.com/help/matlab/ref/rgb2gray.html

https://www.mathworks.com/help/stats/kmeans.html

https://www.mathworks.com/help/matlab/ref/reshape.html

I hope this helps you get started with applying the k-means clustering algorithm for ultrasound image segmentation!