Tumor segmentation in brain MRI using a fuzzy approach with class center priors

This paper proposes a new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed approach reformulates the popular fuzzy c-means (FCM) algorithm to take into account any available information about the class center. The uncertainty in this information is also modeled. This information serves to regularize the clusters produced by the FCM algorithm thus boosting its performance under noisy and unexpected data acquisition conditions. In addition, it also speeds up the convergence process of the algorithm. Experiments using simulated and real, both normal and pathological, MRI volumes of the human brain show that the proposed approach has considerable better segmentation accuracy, robustness against noise, and faster response compared with several well-known fuzzy and non-fuzzy techniques reported in the literature.