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Automatic Thresholding

3.2 | 6 ratings Rate this file 14 Downloads (last 30 days) File Size: 1.26 KB File ID: #5389 Version: 1.0

Automatic Thresholding



01 Jul 2004 (Updated )

How to find a good default threshold value?

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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

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.


Automatic Thresholding inspired this file.

This file inspired Ridler Calvard Image Thresholding.

MATLAB release MATLAB 6.5 (R13)
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Comments and Ratings (7)
30 Dec 2015 Wookyung

too bad

26 Jul 2012 Christina

20 Oct 2009 Atta-Ul Ghaffar

Not too bad. I found the following error on line 27:
mat = sum(count(T_i:end) .* intensity(T_i:end) ) ./ sum(count(T_i:end));

The calculations should run from, T_i + 1 : end

23 Feb 2009 neftirini isabel

what's <nb_of_bins>); ?

Comment only
21 Jul 2008 hanno walter

perfect for me

10 Mar 2008 Farry anto

not good,too bad.

13 Jan 2005 Senes Yin

So Simple!

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