Dhanesh Ramachandram posted on same algorithm, march 2003.
This iterative technique for choosing a threshold was developed by Ridler and Calvard . The histogram is initially segmented into two parts using a starting threshold value such as 0 = 2B-1, half the maximum dynamic range.
The sample mean (mf,0) of the gray values associated with the foreground pixels and the sample mean (mb,0) of the gray values associated with the background pixels are computed. A new threshold value 1 is now computed as the average of these two sample means. The process is repeated, based upon the new threshold, until the threshold value does not change any more.
(quote from http://www.ph.tn.tudelft.nl/Courses/FIP/frames/fip-Segmenta.html)
New feature from the m-file of Dhanesh Ramachandram:
- one does not have to rescale one's image to a uint array. This algorithm works for negative intensities, for example.
vImage = Image(:);
[n xout]=hist(vImage, <nb_of_bins>);
threshold = isodata(n, xout)
You get a (hopefully relevant) threshold for your image.
Gauthier Fleutot (2022). Automatic Thresholding (https://www.mathworks.com/matlabcentral/fileexchange/5389-automatic-thresholding), MATLAB Central File Exchange. Retrieved .
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