Multimodal histogram segmentation in image processing
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1)Select an initial estimate for T 2)Segment the image using T. This will produce two groups of pixels. G1 consisting of all pixels with gray level values >T and G2 consisting of pixels with values <=T. 3)Compute the average gray level values mean1 and mean2 for the pixels in regions G1 and G2. 4)Compute a new threshold value T=(1/2)(mean1 +mean2) 5)Repeat steps 2 through 4 until difference in T in successive iterations is smaller than a predefined parameter T0 Please give me a matlab code for this algorithm
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Accepted Answer
Image Analyst
on 4 Feb 2014
Do not use mean2 as the variable name - that is a function built in to the Image Processing Toolbox. What happens if you just take it one step at a time, like it's trying to walk you through?
% 1)Select an initial estimate for T
T = 128;
T0 = .5;
% 2)Segment the image using T. This will produce two
% groups of pixels. G1 consisting of all pixels with gray
% level values >T and G2 consisting of pixels with values <=T.
G1 = grayImage > T;
G2 = grayImage <= T;
% 3)Compute the average gray level values mean1 and
% mean2 for the pixels in regions G1 and G2.
meanGL1 = mean(grayImage(G1))
meanGL2 = mean(grayImage(G2))
% 4)Compute a new threshold value
Tnew=(1/2) * (meanGL1 +meanGL2)
if (Tnew - T) < T0
and so on. You just need to put that into a while loop and break when the condition of little change is met. I practically did the whole thing for you. You just have to add 4 lines of code.
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Image Analyst
on 25 Apr 2014
You might or might not need to use a Gaussian filter. Can you post your image and tell me what you want to measure?
More Answers (1)
osama
on 22 Apr 2014
hello interesting .. i a;lready working on it ,,,, but where is the using of histogram in ur code ?? i wiating response
greetings
6 Comments
Image Analyst
on 25 Sep 2014
Yes. You could threshold each spectral image. You can combine the thresholded binary images if needed.
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