A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy C-means (FCM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity nonuniformity and thus we propose a novel FCM algorithm named adaptive FCM with neighborhood membership (FC$\mathrm{M}_{-}$anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FCM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.