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

References :
[1]. Martha E.Shenton, Guido Gerig, Robert W.McCarley, Gabor Szekely, Ron Kikinis “Amygdala – hippocampal shape differences in schizophrenia: The application of 3D shape models to volumetric MR data” Elsevier 5 April 2002.
[2]. E.Geuze, E.Vermetten and JD Bremner: “MR – based in vivo hippocampal volumetrics: Findings in neuropsychiatric disorders, Molecular Psychiatry 10, 2005.
[3]. Chupin M., Hasboun D., Poupon F., Baillet S., Garnero L. “Segmentation of the amygdalo – hippocampal complex by competitive region growing [MRI analysis], IEEE International Symposium, 2002.
[4]. Andreasen, N.C., Flashman, L., Flaum, M., Arndt, S., Swayze II, V., O’Leary, D.S., Ehrhardt, J.C., Yuh, W.T.C., 1994. “Regional brain abnormalities in schizophrenia measured with magnetic resonance imaging”. JAMA 272, 1763 – 1769.
[5]. Angenent, S., Haker, S., Tannenbaum, A., Kikinis, R., 1999. “On the Laplace – Beltrami operator and brain surface flattening”. IEEE Transactions on Medical Imaging 18, 700 – 711.
[6]. Attali, D., Montanvert, A., 1994. “Semicontinuous skeletons of 2D and 3D shapes”, proceedings from the Second International Workshop on Visual Form pp 32 – 41.
[7]. Bartley, A.J., Jones, D.W., Weinberger, D.R., 1997. “Genetic variability of human brain size and cortical gyral patterns” Brain 120, 257 – 269.
[8]. Blum, H., 1973. “Biological shape and visual science”. Journal of Theoretical Biology 38, 205 – 287.
[9]. Sethian, J.A., “A fast Marching Level Set Method for Monotonically Advancing Fronts” Proc. Nat. Acad. Sci., 93, 4, pp, 1591 -1595, 1996.