Glioma identification from brain MRI using superpixels and FCM clustering

This work presents a superpixel based computer aided diagnosis (CAD) system for brain tumor segmentation, classification, and identification of glioma tumors. It utilizes superpixel and fuzzy c-means clustering concept for tumor segmentation. At first, dataset images are preprocessed by anisotropic diffusion and dynamic stochastic resonance-based enhancement technique and further segmented through the proposed concept. The run length of centralized patterns are extracted from the segmented regions and classified with naive Bayes classifier. The performance of the system is examined on two brain magnetic resonance imaging datasets for segmentation and identification of glioma tumors. Accuracy for tumor detection is observed 99.89% on JMCD dataset and 100% on BRATS dataset. For glioma identification average accuracies are observed as 97.94% and 98.67% on JMCD and BRATS dataset, respectively. The robustness of the system is examined by 10-fold cross validation and statistical testing. Outcomes are also verified by domain experts in real time.