DocumentCode :
3243805
Title :
MRI brain classification using support vector machine
Author :
Othman, Mohd Fauzi Bin ; Abdullah, Noramalina Bt ; Kamal, Nurul Fazrena Bt
Author_Institution :
Centre for Artificial Intell. & Robot. (CAIRO), Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia
fYear :
2011
fDate :
19-21 April 2011
Firstpage :
1
Lastpage :
4
Abstract :
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed.
Keywords :
biomedical MRI; brain; discrete wavelet transforms; image classification; medical image processing; support vector machines; tumours; MRI brain classification; SVM; brain tumor; classification system; discrete wavelet transformation; kernel based technique; magnetic resonance imaging; medical image retrieval system; medical imaging; neuroscience; support vector machine; Biomedical imaging; Brain; Feature extraction; Magnetic resonance imaging; Support vector machines; Wavelet transforms; Brain Tumor; Classification; MRI; SVM; Wavelet;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4577-0003-3
Type :
conf
DOI :
10.1109/ICMSAO.2011.5775605
Filename :
5775605
Link To Document :
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