K-means segmentation
Hello. I have a grayscale image with a mole and skin which I want to segment with K-means algorithm.I want the mole pixels to be classified in class 1 and the skin pixels to be classified in class2. How can I do that? The code above works, but sometimes the mole is black and sometimes is white. I want this to be done with k-means segmentation, I know it can be done in other different ways.
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The class numbers that kmeans() assigns can vary from one run to the next because it uses random numbers. However you can renumber the class labels if you know something about the class, like you always want class 1 to be the darker class, and class 2 to be the lighter class. See demo Code .
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% Demo to show how you can redefine the class numbers assigned by kmeans() to different numbers.
% In this demo, the original, arbitrary class numbers will be reassigned a new number
% according to how far the cluster centroid is from the origin.
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clearvars;
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 18;%===========================================================================================================================================
% DEMO #1 : RELABEL ACCORDING TO DISTANCE FROM ORIGIN.
%===========================================================================================================================================%-------------------------------------------------------------------------------------------------------------------------------------------
% FIRST CREATE SAMPLE DATA.
% Make up 4 clusters with 150 points each.
pointsPerCluster = 150;
spread = 0.03;
offsets = [0.3, 0.5, 0.7, 0.9];
% offsets = [0.62, 0.73, 0.84, 0.95];
xa = spread * randn(pointsPerCluster, 1) + offsets(1);
ya = spread * randn(pointsPerCluster, 1) + offsets(1);
xb = spread * randn(pointsPerCluster, 1) + offsets(2);
yb = spread * randn(pointsPerCluster, 1) + offsets(2);
xc = spread * randn(pointsPerCluster, 1) + offsets(3);
yc = spread * randn(pointsPerCluster, 1) + offsets(3);
xd = spread * randn(pointsPerCluster, 1) + offsets(4);
yd = spread * randn(pointsPerCluster, 1) + offsets(4);
x = [xa; xb; xc; xd];
y = [ya; yb; yc; yd];
xy = [x, y];%-----------------------
SEE COMPLETE ANSWER CLICK THE LINK
