# K means Segmentation function

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Answered: Sanju on 22 Feb 2024
clc
T= rgb2lab(img);
T = im2uint8(T);
nrows = size(T,1);
ncols = size(T,2);
[X,Y] = meshgrid(1:ncols,1:nrows);
I = cat(3,T,X,Y);
K = 6;
P = imsegkmeans(T,K);
Z = labeloverlay(T,P);
L = imsegkmeans(I,K);
J = labeloverlay(T,L);
imshowpair(Z,J,'Montage');
I need to create a fucntion which replicates what imsegkmeans is doing right in this code. Could someone please explain with code.
@Image Analyst can you kindly help me with this?

Sanju on 22 Feb 2024
The ‘imsegkmeans’ function in MATLAB performs k-means clustering on an image to segment it into ‘K’ distinct regions.
Here’s how you can create a function that replicates the functionality of ‘imsegkmeans’,
function P = myImsegkmeans(I, K)
% Convert the input image to double precision
I = im2double(I);
% Reshape the image into a 2D matrix
[nrows, ncols, ~] = size(I);
X = reshape(I, nrows*ncols, []);
% Perform k-means clustering
[~, C] = kmeans(X, K);
% Assign each pixel to the nearest cluster centroid
[~, P] = pdist2(C, X, 'euclidean', 'Smallest', 1);
P = reshape(P, nrows, ncols);
end
In this function, ‘I’ is the input image and ‘K’ is the desired number of clusters.
The function first converts the image to double precision using ‘im2double’. Then, it reshapes the image into a 2D matrix where each row represents a pixel and its color values. The ‘kmeans’ function is used to perform k-means clustering on the reshaped image, and the resulting cluster centroids are stored in ‘C’. Finally, each pixel in the image is assigned to the nearest cluster centroid using the ‘pdist2’ function, and the cluster assignments are reshaped back into the original image size.
You can use this function in your code by replacing the ‘imsegkmeans’ function call with ‘myImsegkmeans’,
P = myImsegkmeans(T, K);
You can also refer to the below documentation link if required,
Hope this Helps!

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