Segmentation Technique to Detect the AD in Hippocampus Shape using Region Growing in Support Vector Machine

R. Viswanathan¹ , K. Perumal²

1. Dept. of Information Technology and Management, Arul Anandar College, Karumathur, India.
2. Dept. of Computer Application (School of Information Technology), Madurai Kamaraj University, Madurai, India.

Correspondence should be addressed to: viswa_for_you@yahoo.co.in.

Section:Review Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.56-59, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i1.5659

Online published on Feb 28, 2018

Copyright © R. Viswanathan, K. Perumal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The main cause for AD is low brain activity and blood flow. The important component of human brain is hippocampus; here the segmentation technique is used for medical images on real set of Alzheimer’s disease Patients. The normal behavior is depends on functionality of hippocampus. There are various techniques available for image processing in segmentation, in which region growing is used for segmenting the hippocampus region. The brain images are converted into binary form in two approaches. The first approach is based upon block mean and mask approach. The second approach is on top hat and mask. In this some part of images contains hole in which it interrupts the segmentation process. To overcome this problem image hole filtering techniques are implemented. The shape analysis structure of hippocampus will result in classifying the Alzheimer’s disease in human brain on Support Vector Machines.

Key-Words / Index Term :
Hippocampus; Segmentation; Alzheimer’s disease; SVM classifier

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