function DemoFCM

% Segment a sample 2D image into 3 classes using fuzzy c-means algorithm. 

% Note that similar syntax would be used for c-means based segmentation,

% except there would be no fuzzy membership maps (denoted by variable U 

% below).

im=imread('001_1.tif'); % sample image

[C,U,LUT,H]=FastFCMeans(im,3); % perform segmentation

% Visualize the fuzzy membership functions

figure('color','w')

subplot(2,1,1)

I=double(min(im(:)):max(im(:)));

c={'-r' '-g' '-b'};

for i=1:3

    plot(I(:),U(:,i),c{i},'LineWidth',2)

    if i==1, hold on; end

    plot(C(i)*ones(1,2),[0 1],'--k')

end

xlabel('Intensity Value','FontSize',30)

ylabel('Class Memberships','FontSize',30)

set(gca,'XLim',[0 260],'FontSize',20)

subplot(2,1,2)

plot(I(:),LUT(:),'-k','LineWidth',2)

xlabel('Intensity Value','FontSize',30)

ylabel('Class Assignment','FontSize',30)

set(gca,'XLim',[0 260],'Ylim',[0 3.1],'YTick',1:3,'FontSize',20)

% Visualize the segmentation

figure('color','w')  

subplot(1,2,1), imshow(im)

set(get(gca,'Title'),'String','ORIGINAL')

L=LUT2label(im,LUT);

Lrgb=zeros([numel(L) 3],'uint8');

for i=1:3

    Lrgb(L(:)==i,i)=255;

end

Lrgb=reshape(Lrgb,[size(im) 3]);

subplot(1,2,2), imshow(Lrgb,[])

set(get(gca,'Title'),'String','FUZZY C-MEANS (C=3)')

% If necessary, you can also unpack the membership functions to produce 

% membership maps

Umap=FM2map(im,U,H);

figure('color','w')

for i=1:3

    subplot(1,3,i), imshow(Umap(:,:,i))

    ttl=sprintf('Class %d membership map',i);

    set(get(gca,'Title'),'String',ttl)

end