Research Article
A Robust Brain MRI Classification with GLCM Features
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 37 - Issue 12 |
| Published: January 2012 |
| Authors: Sahar Jafarpour, Zahra Sedghi, Mehdi Chehel Amirani |
10.5120/4735-6872
|
Sahar Jafarpour, Zahra Sedghi, Mehdi Chehel Amirani . A Robust Brain MRI Classification with GLCM Features. International Journal of Computer Applications. 37, 12 (January 2012), 1-5. DOI=10.5120/4735-6872
@article{ 10.5120/4735-6872,
author = { Sahar Jafarpour,Zahra Sedghi,Mehdi Chehel Amirani },
title = { A Robust Brain MRI Classification with GLCM Features },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 37 },
number = { 12 },
pages = { 1-5 },
doi = { 10.5120/4735-6872 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Sahar Jafarpour
%A Zahra Sedghi
%A Mehdi Chehel Amirani
%T A Robust Brain MRI Classification with GLCM Features%T
%J International Journal of Computer Applications
%V 37
%N 12
%P 1-5
%R 10.5120/4735-6872
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Automated and accurate classification of brain MRI is such important that leads us to present a new robust classification technique for analyzing magnetic response images. The proposed method consists of three stages, namely, feature extraction, dimensionality reduction, and classification. We use gray level co-occurrence matrix (GLCM) to extract features from brain MRI and for selecting the best features, PCA+LDA is implemented. The classifiers goal is to classify subjects as normal and abnormal brain MRI. A classification with a success of 100% for two normal and abnormal classes is obtained by the both classifiers based on artificial neural network (ANN) and k-nearest neighbor (k-NN). The proposed method leads to a robust and effective technique, which reduces the computational complexity, and the operational time compared with other recent works.
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