How to run the following matlab code?

Hi Experts,

I have take the following code from file exchange, credits: ankit dixit

Can any one help me how to run this code, i have a gray scale image in my computer and i want to get an output clustered image using this code. Please explain with simplicity as i m new to matlab.

function [lb,center] = adaptcluster_kmeans(im)

% This code is written to implement kmeans clustering for segmenting any
% Gray or Color image. There is no requirement to mention the number of cluster for
% clustering. 
% IM - is input image to be clustered.
% LB - is labeled image (Clustered Image).
% CENTER - is array of cluster centers.
% Execution of this code is very fast.
% It generates consistent output for same image.

% Written by Ankit Dixit.
% January-2014.

if size(im,3)>1
   [lb,center] = ColorClustering(im); % Check Image is Gray or not.
else
    [lb,center] = GrayClustering(im); 
end

function [lb,center] = GrayClustering(gray)
gray = double(gray);
array = gray(:); % Copy value into an array.
% distth = 25;
i = 0;j=0; % Intialize iteration Counters.
tic
while(true)
    seed = mean(array); % Initialize seed Point.
    i = i+1; %Increment Counter for each iteration.
    while(true)
        j = j+1; % Initialize Counter for each iteration.
        dist = (sqrt((array-seed).^2)); % Find distance between Seed and Gray Value.
        distth = (sqrt(sum((array-seed).^2)/numel(array)));% Find bandwidth for Cluster Center.
        %         distth = max(dist(:))/5;
        qualified = dist<distth;% Check values are in selected Bandwidth or not.
        newseed = mean(array(qualified));% Update mean.

          if isnan(newseed) % Check mean is not a NaN value.
              break;
          end

          if seed == newseed || j>10 % Condition for convergence and maximum iteration.
              j=0;
              array(qualified) = [];% Remove values which have assigned to a cluster.
              center(i) = newseed; % Store center of cluster.
              break;
          end
          seed = newseed;% Update seed.
      end

      if isempty(array) || i>10 % Check maximum number of clusters.
          i = 0; % Reset Counter.
          break;
      end

end
toc

center = sort(center); % Sort Centers.
newcenter = diff(center);% Find out Difference between two consecutive Centers. 
intercluster = (max(gray(:)/10));% Findout Minimum distance between two cluster Centers.
center(newcenter<=intercluster)=[];% Discard Cluster centers less than distance.

% Make a clustered image using these centers.

vector = repmat(gray(:),[1,numel(center)]); % Replicate vector for parallel operation.
centers = repmat(center,[numel(gray),1]);

distance = ((vector-centers).^2);% Find distance between center and pixel value.
[~,lb] = min(distance,[],2);% Choose cluster index of minimum distance.
lb = reshape(lb,size(gray));% Reshape the labelled index vector.

function [lb,center] = ColorClustering(im)

im = double(im);
red = im(:,:,1); green = im(:,:,2); blue = im(:,:,3);

array = [red(:),green(:),blue(:)];
% distth = 25;
i = 0;j=0;
tic
while(true)

      seed(1) = mean(array(:,1));
      seed(2) = mean(array(:,2));
      seed(3) = mean(array(:,3));

      i = i+1;
      while(true)
          j = j+1;

          seedvec = repmat(seed,[size(array,1),1]);

          dist = sum((sqrt((array-seedvec).^2)),2);

           distth = 0.25*max(dist);
          qualified = dist<distth;

          newred = array(:,1);
          newgreen = array(:,2);
          newblue = array(:,3);

          newseed(1) = mean(newred(qualified));
          newseed(2) = mean(newgreen(qualified));
          newseed(3) = mean(newblue(qualified));

          if isnan(newseed)
              break;
          end

          if (seed == newseed) | j>10
              j=0;
              array(qualified,:) = [];
              center(i,:) = newseed;
              %             center(2,i) = nnz(qualified);
              break;
          end
          seed = newseed;
      end

      if isempty(array) || i>10
          i = 0;
          break;
      end

end
toc
centers = sqrt(sum((center.^2),2));
[centers,idx]= sort(centers);

while(true)
newcenter = diff(centers);
intercluster =25; %(max(gray(:)/10));
a = (newcenter<=intercluster);
% center(a,:)=[];
% centers = sqrt(sum((center.^2),2));
centers(a,:) = [];
idx(a,:)=[];
% center(a,:)=0;
if nnz(a)==0
    break;
end

end
center1 = center;
center =center1(idx,:);
% [~,idxsort] = sort(centers) ;
vecred = repmat(red(:),[1,size(center,1)]);
vecgreen = repmat(green(:),[1,size(center,1)]);
vecblue = repmat(blue(:),[1,size(center,1)]);

distred = (vecred - repmat(center(:,1)',[numel(red),1])).^2;
distgreen = (vecgreen - repmat(center(:,2)',[numel(red),1])).^2;
distblue = (vecblue - repmat(center(:,3)',[numel(red),1])).^2;

distance = sqrt(distred+distgreen+distblue);
[~,label_vector] = min(distance,[],2);
lb = reshape(label_vector,size(red));
%

[Geoff Hayes]

Rizwan - simply attaching the m file (using the paperclip button) or referencing the code in the File Exchange (by providing the link) would have been sufficient (instead of copying and pasting all of the code).

Note that the function signature is simply

 [lb,center] = adaptcluster_kmeans(im)

where im is the image. So use imread to read the image from file as

 filename = '/Users/x/.../test.jpg';  % example only - use your file name
 myImg = imread(filename);

and then run the Kmeans clustering function as

 [lb,center] = adaptcluster_kmeans(myImg);