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Classification of Normal and Abnormal Brain Volume and Surface Area using Single Point Thresholding

by Shweta Tripathi, Roshan Jain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 13
Year of Publication: 2014
Authors: Shweta Tripathi, Roshan Jain
10.5120/15409-3702

Shweta Tripathi, Roshan Jain . Classification of Normal and Abnormal Brain Volume and Surface Area using Single Point Thresholding. International Journal of Computer Applications. 88, 13 ( February 2014), 1-5. DOI=10.5120/15409-3702

@article{ 10.5120/15409-3702,
author = { Shweta Tripathi, Roshan Jain },
title = { Classification of Normal and Abnormal Brain Volume and Surface Area using Single Point Thresholding },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 13 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number13/15409-3702/ },
doi = { 10.5120/15409-3702 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:29.308309+05:30
%A Shweta Tripathi
%A Roshan Jain
%T Classification of Normal and Abnormal Brain Volume and Surface Area using Single Point Thresholding
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 13
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the most important subjects in the processing MR image is segmentation, especially extraction of the brain regions, which is part of the decision of urgent operation on brain. This type of medical operations need speed up process with maximum accuracy. In this study, brain is segmented by using k-means algorithm. A combination of global, adaptive thresholding techniques and at the next stage morphological operations was used for pre-processing. Moreover after these stage the main aim was setting out in the regional different of specified brain disorders to detect normality or abnormality. MRI neuroimages were used. The parameters were slices consisted of 1. 5 mm thickness dual-echo fast spin echo data sets that are acquired through MRI scanners. The quality and robustness of the results of this study depend on the homogeneity of MRIs. Finally neuroimages were segmented to gray matter and white matter and volumetric measurements were calculated for whole brain and of these issue types.

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Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Volumetric Analysis Gyrification Index MRI Autism Thresholding algorithms K-means White matter

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