how to converts a gray scale image (All pixels from 0 to 255) to a black and white image (All pixels either 0 or 1)?

this my given code, and I want to complete it by C-means clustering :

please read the code, and give me the complementary code.

thanx in advance..

% read image matrix from file
Img=imread('C:\Users\sony\Desktop\fp.png');
% convert to gray scale (remove color info)
Img = rgb2gray(Img);
% show the fingerprint image in a window
figure(1); imshow(Img);
% convert image data to double precision format
ImgD=double(Img);
% convert matrix data to single column vector format
% namely arrange all numbers in the matrix as a single column vector
grades=ImgD(:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DO YOUR CLUSTERING HERE !
% For illustrative purposes, I compute the median, and use it to decide pass
% or fail. But you should do clustering and decide about the threshold !
t = median(grades);
% This selection will not result a good B&W image at the end.
% Your method should result a better B&W image !
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% generate a new image matrix in memory
newImg = zeros(size(Img));
for row=1:size(Img,1)
    for col=1:size(Img,2)
        if (ImgD(row,col) < t)
            newImg(row,col) = 0; % below threshold fails
        else
            newImg(row,col) = 1; % above threshold passes
        end
    end
end
figure(2); imshow(newImg);

[dpb]

If all you're going to do is fixed threshold there's no need for looping...

newImg=ones(size(ImgD);  % start with all white
ix=(ImgD<t);             % 2D logical array 
newImg(ix)=0;            % below threshold fails

Or, of course, since the result of logic test is 0,1 already, then even more concise is

newImg=uint8(ImgD>=t);   % cast to whatever data type desired

Answer 1

[Image Analyst]

You can either do thresholding like dpb showed above in his comment, or use some other kind of algorithm, like imbinarize() or similar. If you want to do a statistical clustering, see my attached kmeans demo.

Generally kmeans does a bad job of color segmentation. I don't have the Fuzzy Toolbox so I can't help you with fuzzy c-means classification.