MRI Brain Tumor Segmentation and Analysis using Rough-Fuzzy C-Means and Shape Based Properties

Abstract Automated brain tumor segmentation of MR image is a very challenging task in a medical point of view. As the nature of the tumor, it can appear anywhere in the brain region with any size, shape, and contrast, that makes the segmentation process more difficult. In order to handle such issues, present work proposes an automated brain tumor segmentation method using rough-fuzzy C-means (RFCM) and shape based topological properties. In rough-fuzzy C-means, overlapping partition is efficiently handled by fuzzy membership and uncertainty in the datasets is resolved by lower and upper bound of the rough set. Fuzzy boundary and crisp lower approximation in RFCM play an effective contribution in brain tumor segmentation on MR images. Initial centroids selection is a major issue in C-means algorithms. Present work has introduced a method for initial centroids selection by which the execution time of RFCM is reduced as compared to random initial centroids. A patch based K-means method is also implemented for skull stripping as a preprocessing step. The proposed method was tested on MRI standard benchmark datasets. Experimental results show that the proposed method has achieved better performance based on statistical volume metrics than previous state-of-the-art algorithms with respect to ground truth (manual segmentation). It is also experimentally noticed that RFCM method achieves most promising results with higher accuracy than HCM (hard C-means) and FCM (fuzzy C-means).