Robust fuzzy clustering using nonsymmetric student׳s t finite mixture model for MR image segmentation

Accurate tissue segmentation from magnetic resonance (MR) images is an essential step in clinical practice. In this paper, we introduce a robust fuzzy clustering scheme for finite mixture model fitting, which exploits the merits of the mixture of the nonsymmetric Student׳s t-distribution and mean template to reduce the sensitivity of the segmentation results with respect to noise. This approach utilizes a fuzzy objective function regularized by the Kullback–Leibler (KL) divergence term and sets the dissimilarity function as the negative log-likelihood of the nonsymmetric Student׳s t-distribution and mean template. The advantage of this fuzzy clustering scheme is that the spatial relationships among neighbouring pixels are taken into account with the help of the mean template so that the proposed method is more robust to noise than several other existing fuzzy c-means (FCM)-based algorithms. Another advantage is that the application of the nonsymmetric Student׳s t-distribution mixture model allows the proposed model to fit different shapes of observed data. Experiments using synthetic and real MR images show that the proposed model has considerably better segmentation accuracy and robustness against noise compared with several well-known finite mixture models.