I have the matlab code for Hierarchical Centroid Shape Descriptor method. The image contains the steps .Please help me to correcte the codes for brain tumor detection.

Here by i attached the steps in the imaege and i have also attached the coding . this is the HCSD code for letters only. How can i modify the code by using the steps mentioned in my image..

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This refers to
'Automatic brain tumour tissue detection based on Hierarchical Centroid Shape Descriptor using T1-weighted MRI'
Actually am new to matlab and i have done the coding upto K-means clustering. Now i didn't got any idea about this HSCD. Can you please help me for writing the codes for HCSD based on the information that i have attached above..
Unless you come up with more specific questions, then I am going to tend to assess that what you are really asking for is for someone to write your project for you.
Please check this code..I want to convert this code for Brain MRI tumor detection..
It looks to me as if you could call the code directly, provided that you are passing in a 2D image.
Yes. It is the HCSD method for Letters. I want to make the code for brain tumor MRI input.

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Answers (1)

See my tumor detection demo. Note: it works for this image but is not guaranteed to work with all images that have tumors, of course. But feel free to adapt it.
For what it's worth, I'm also attaching my kmeans demo, though I said in your other question it's not good as a complete method for finding tumors unless you know in advance there there is definitely one there.

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Thank you ..Yes, it is very sure that the kmeans may sometimes select tumor and some other sorrounding tissue, for extractiong such healthy tissues , the HCSD method will be used and it will worked based on this attached source code (have to modify the code for brain tumor).The HCSD is decompose the image in subimages. There will be five steps
1.Take the input I and compute the transpose of I. 2.Calculate the centroid for each input. 3.Divide the image into two sub images based on the centers of gravity. 4.Normalize the obtained vector by using[-0.5,0.5] 5.Concatenate the features extratced from I and transpose of I
Then the healthy tissues are removed from the mri and select the tumor section alone

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on 7 Feb 2017

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on 28 Feb 2017

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