A robust nonsymmetric student´s-t finite mixture model for MR image segmentation

In this paper, a nonsymmetric Student´s-t distribution model is proposed for magnetic resonance (MR) image segmentation based on Markov random field (MRF) and weighted mean template. The presented nonsymmetric Student´s-t distribution with longer tails and one more parameter compared to Gaussian distribution is implemented because in real applications, the distribution of data set does not totally follow symmetric distribution. Thus, our method fits much closer to different shapes of observed data. With the help of MRF and weighted mean template, the spatial information is also taken into consideration in MR image segmentation. Then, the expectation-maximization (EM) algorithm is introduced to solve the problem of parameter learning. The accuracy and effectiveness of the proposed method is quantitatively assessed in both simulated and clinical MR images.