Computer aided brain tumor detection system using watershed segmentation techniques

Magnetic Resonance Imaging (MRI) is an advanced medical imaging technique that has proven to be an effective tool in the study of the human brain. In this article, the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification. Histogram equalization is used in enhancement stage, gray level co-occurrence matrix and wavelets are used as features and these extracted features are trained and classified using Support Vector Machine (SVM) classifier. The tumor region is detected using morphological operations. The performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The proposed system achieved 0.95% of sensitivity rate, 0.96% of specificity rate, 0.94% of accuracy rate, 0.78% of PPV, and 0.87% of NPV, respectively. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 297–301, 2015