Hybridized Classification of Brain MRI using PSO & SVM
Amita Kumari1, Rajesh Mehra2
1Amita Kumari, ECE Department, NITTTR, Chandigarh, India.
2Rajesh Mehra, ECE Department, NITTTR, Chandigarh, India.
Manuscript received on April 02, 2014. | Revised Manuscript received on April 26, 2014. | Manuscript published on April 30, 2014. | PP: 319-323  | Volume-3, Issue-4, April 2014. | Retrieval Number:  D3008043414/2013©BEIESP

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Magnetic resonance imaging (MRI) provides detailed anatomic information of any part of the body. In this method a hybrid approach for classification of brain tissue in MRI based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) wavelet based texture feature are extracted from normal and tumor region by using HAAR wavelet. These features are given as input to the SVM classifier which classified them into normal & abnormal brain neoplasm. The algorithm incorporates steps for pre-processing, image segmentation and image classification using SVM classifier.
Keywords: MRI, Classification PSO, SVM, HAAR wavelet.