Identification of Gliomas from brain MRI through adaptive segmentation and run length of centralized patterns

Brain tumor detection and identification of its severity is a challenging task for radiologists and clinicians. This work aims to develop a novel clinical decision support system to assist radiologists and clinicians efficiently in real-time. The proposed clinical decision support system utilizes fusion of MRI pulse sequences as each of them gives salient information for tumor identification. An adaptive thresholding is proposed for segmentation and centralized patterns are observed from LBP image of so obtained segmented image. Run length matrix extracted from these centralized patterns is used for tumor identification. The developed features successfully identify and classify tumor with Naive Bayes classifier. The proposed decision support system not only detects tumors, but also identifies its grading in terms of severity. As Glioma tumors are the most frequent among brain tumors, the proposed system is tested for the presence of low grade (Astrocytoma and Ependymoma) as well as high grade (Oligodendroglioma and Glioblastoma Multiforme) Glioma tumors on images collected from NSCB Medical College Jabalpur, India and BRATS dataset. The experiments performed on two datasets give more than 96% accuracy. The proposed decision support system is quite sensitive towards the detection and specification of tumors. All the results are verified by domain experts in real time.