Glioma detection on brain MRIs using texture and morphological features with ensemble learning

The real time usage of Computer Aided Diagnosis (CAD) systems to detect brain tumors as proposed in the literature is yet to be explored. Gliomas are the most commonly found brain tumors in human. The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors. The system is devised to work irrespective of the image pulse sequence. It uses different segmentation schemes for different pulse sequences, fusion of texture features, and ensemble classifier to perform three levels of classification. Once the tumor is detected at the first level of classification, its location is analyzed using tentorium of brain and it is classified into superatentorial or infratentorial in the next level. Based on the morphological and inherent characteristics of tumor (area, perimeter, solidity, and orientation), the system identifies tumor type at the third level of classification. The system reports average accuracy of 97.76% on JMCD (a dataset collected from local medical college) and 97.13% on BRATS datasets at the first level of classification. Average accuracy of 97.87% for astrocytomas, 94.24% for ependymoma, 96.29% for oligodendroglioma, and 98.69% for glioblastoma multiforme is observed for histologically classified JMCD dataset. The same is observed as 95.45% for low grade and 95.50% for high grade tumors in publically available BRATS dataset. The performance of the proposed CAD system is statistically examined through hypothetical Student's t-test and Wilcoxon matched pair test. The performance of the system is also validated by domain experts for its possible real time usage.