International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 37 - Issue 12 |
Published: January 2012 |
Authors: Sahar Jafarpour, Zahra Sedghi, Mehdi Chehel Amirani |
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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
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.