A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu's thresholding with prominent features and supervised learning

The detection of brain tumor is a challenging task for radiologists as brain is the most complicated and complex organ. This work presents a non-invasive and adaptive method for detection of tumor from T2-weighted brain magnetic resonance (MR) images. Non-homogeneous brain MR images are enhanced by preprocessing and segmented through multilevel customization of Otsu's thresholding technique. Several textural and shape features are extracted from the segmented image and two prominent ones are selected through entropy measure. Support vector machine (SVM) classifies MR images using prominent features. Experiments are performed on a dataset collected from MP MRI & CT Scan Centre at NSCB Medical College Jabalpur and the other from Charak Diagnostic & Research Centre Jabalpur. More than 98% accuracy is reported with 100% sensitivity for both the datasets at 99% confidence interval. The proposed system is compared with several existing methods to showcase its efficacy.