/image_analysisII/bookcode/visionbook/06Segm1/imthresh.m

function [out,threshold]=imthresh(im)

%IMTHRESH Iterative (optimal) image thresholding.

% CMP Vision Algorithms http://visionbook.felk.cvut.cz

%  

% Threshold a grayscale image using an automatic iterative threshold selection

% . 

%  

% Usage: [out,threshold] = imthresh(im)

% Inputs:

%  im   [m x n]  Input grayscale image with intensities 0... 255.

% Outputs:

%   out  [m x n]  Binary output image, contains ones where the pixel

%     values of im are above or equal to the threshold and

%     zeros elsewhere.

%  threshold  1x1  Threshold value.

% 







% We pre-calculate the histogram and cumulative histogram. This way

% we can evaluate in constant time the mean of the pixels above or below

% threshold. 

histogram = hist( im(:), 0:255 );

hist_times_gray = cumsum( histogram.*[0:255] );

cumulative_histogram = cumsum( histogram );



% A first approximation of the background mean mean_1 is  

% the mean of the corner pixels. A first approximation of the foreground

% mean mean_2 is the mean of the rest of the image. 

% The initial threshold

% is set as an average of the two means. 

[m,n] = size(im);

sum_background = sum( im([1 m  n*(m-1)+1  n*m]) );

num_pix_background = 4;

mean_1 = sum_background / num_pix_background;

mean_2 = (sum(im(:))-sum_background) / (n*m-num_pix_background);

threshold = ceil( (mean_1+mean_2)/2 );



if (threshold~=0)&&(cumulative_histogram(threshold)==0)

    threshold_old=threshold;

end; 



% We calculate new foreground and background means defined by the 

% threshold and use them to calculate the new threshold, repeating until

% convergence. The procedure is very fast because the actual thresholding

% only takes place once the final threshold is found.

threshold_old = 0; 

while threshold~=threshold_old

  threshold_old = threshold;

  mean_1 = hist_times_gray(threshold) / cumulative_histogram(threshold);

  mean_2 = (hist_times_gray(end)-hist_times_gray(threshold+1)) / ...

           (cumulative_histogram(end)-cumulative_histogram(threshold+1));

  threshold = ceil( (mean_1+mean_2)/2 );

end

out = im>=threshold;