how to obtain pixel values of cluster

sir, i have done k means clustering on an image.By using imagesc command ,i can view he image and see 3 clusters. i used adaptivecluster function from mathworks.it works well.What i want is that i want to delete the one set of cluster or want the pixel values in each cluster.please help?

Answer 1

[Image Analyst]

What toolbox is the "adaptivecluster" function in? Please list it in the Products section. A search of the entire Mathworks web site didn't show it anywhere except in your question. And your question does not provide us with enough details to answer. You gave no original image, no clustered image, no code, nothing - so how are we supposed to help, I mean other than referring you to this: http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer?

[raj kumar]

sir, adaptcluster is a function for kmeans clustering.i will give the function and code.

 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)); %

I got this function from this website.after running the code ,I got the following image as output.

I need the pixel values in each cluster.how is it possible??

[raj kumar]

this is the ouput image

[raj kumar]

[raj kumar]

i got the answer.thank you sir.

[Image Analyst]

Well I didn't do much, but you're welcome. By the way, we don't call those clusters, we call them blobs, regions, or connected components. To get their areas you can do

labeledImage = bwlabel(binaryImage);
measurements = regionprops(labeledImage, 'Area');
allAreas = [measurements.Area];

[raj kumar]

sir, do any matlab code avilable to form two clusters of an image using k means algorithm.please provide..

[Image Analyst]

Here's an example using an image and kmeans() from the Mathworks: http://www.mathworks.com/products/demos/image/color_seg_k/ipexhistology.html

Even though you say you got the answer already , it might be instructive to look over their example.