kmeans聚类图像分割 matlab

function [mu,mask]=kmeans(ima,k) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%

%

% kmeans image segmentation

%

% Input:

% ima: grey color image

% k: Number of classes

% Output:

% mu: vector of class means

% mask: clasification image mask

%

% Author: Jose Vicente Manjon Herrera

% Email: jmanjon@fis.upv.es

% Date: 27-08-2005

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%

% check image

ima=double(ima);

copy=ima; % make a copy

ima=ima(:); % vectorize ima

mi=min(ima); % deal with negative

ima=ima-mi+1; % and zero values

s=length(ima);

% create image histogram

m=max(ima)+1;

h=zeros(1,m);

hc=zeros(1,m);

for i=1:s

if(ima(i)>0) h(ima(i))=h(ima(i))+1;end;

end

ind=find(h);

hl=length(ind);

% initiate centroids

mu=(1:k)*m/(k+1);

% start process

while(true)

oldmu=mu;

% current classification

for i=1:hl

c=abs(ind(i)-mu);

cc=find(c==min(c));

hc(ind(i))=cc(1);

end

%recalculation of means

for i=1:k,

a=find(hc==i);

mu(i)=sum(a.*h(a))/sum(h(a));

end

if(mu==oldmu) break;end;

end

% calculate mask

s=size(copy);

mask=zeros(s);

for i=1:s(1),

for j=1:s(2),

c=abs(copy(i,j)-mu);

a=find(c==min(c));

mask(i,j)=a(1);

end

end

mu=mu+mi-1; % recover real range