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International Journal of Biomedical Imaging
Volume 2017 (2017), Article ID 9749108, 12 pages
https://doi.org/10.1155/2017/9749108
Research Article

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

1School of Electronics Engineering, KIIT University, Bhubaneswar, Odisha, India
2Department of Electronics & Telecommunication Engineering, Lovely Professional University, Jalandhar, Punjab, India

Correspondence should be addressed to Nilesh Bhaskarrao Bahadure; moc.liamg@erudahabn

Received 16 January 2017; Accepted 16 February 2017; Published 6 March 2017

Academic Editor: Guowei Wei

Copyright © 2017 Nilesh Bhaskarrao Bahadure et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.