How can I segment brain gray matter from white matter?
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Hi. How can I segment the gray matter from the white matter from the attached image? Please help me with MATLAB codes.
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MarKf
2023 年 8 月 5 日
Your jpeg seems to be a black and white image, you'd need at least a grayscale one to segment gray and white matter (also a neuroscientist may understand what you mean with gm and wm, but this question -same as the pic- is awfully generic).
You can then use a threshold to separate them in a single image. if the image has a gradient or a local bias a local adaptive thresholding method might be useful. There are tools to get the threshold automatically and to create probabilistic maps of the tissues. One example is ft_volumesegment from the fieldtrip toolbox.
回答 (2 件)
Image Analyst
2023 年 8 月 7 日
Find published algorithms here:
Pick one and code it up yourself, or ask the author to buy the code from them.
3 件のコメント
Image Analyst
2023 年 8 月 7 日
Sorry, I don't do private consulting for Answers people.
To learn fundamental concepts, invest 2 hours of your time here:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1452657/image.png)
Walter Roberson
2023 年 8 月 7 日
First of all: you have zero possibility of accurately segmenting a JPEG image (unless it is one of the rare lossless JPEG images). Lossless JPEG blurs edges, and you will not be able to recover from that.
Secondly: A place I used to work spent several years trying to create algorithms to segment brain MRI images, working with brain surgeons and neurophysicts. 72% accuracy we could do routinely; to get past 78% accuracy required a lot of work, at least with real brain MRI.
What we found, in practice, was that getting useful accuracy required switching out of using MRI or CT images, and to instead use MRS -- to effectivelyu do chemical analysis voxel by voxel instead of doing image analysis.
Perhaps our results would have been different if we had had access to Recurrent Neural Network (RNN) technology. Our shallow neural network work did not produce any useful results -- too many false positives, too many false negatives.
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