A Brain MR Images Segmentation and Bias Correction Model Based on Students t-Mixture Model

Accurate segmentation for magnetic resonance images is an essential step in quantitative brain image analysis. However, due to the existence of bias field and noise, many segmentation methods are hard to find accurate results. Finite mixture model is one of the wildly used methods for MR image segmentation; however, it is sensitive to noise and cannot deal with images with intensity inhomogeneity. In order to reduce the effect of noise, we introduce a robust Markov Random Field by incorporating new spatial information which is constructed based on posterior probabilities and prior probabilities. The bias field is modeled as a linear combination of a set of orthogonal basis functions and coupled into the model and makes the method can estimate the bias field meanwhile segmenting images. Our statistical results on both synthetic and clinical images show that the proposed method can obtain more accurate results.

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