Segmentation of Brain MR Image Using Modified Student’s t-Mixture Model

Abstract

In conventional brain image analysis, it is a critical step to segment brain magnetic resonance (MR) image into three major tissues: Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The main difficulties for segmenting brain MR image are partial volume effect, intensity inhomogeneity and noise, which result in challenging segmentation task. In this paper, we propose a novel modified method based on the basis of the conventional Student’s t-Mixture Model (SMM), for segmentation of brain MR image and correction of bias field. The advantages of our model are introduced as follows. First, we take account of the influence on the probabilities of the pixels in the adjacent region and take full advantage of the local spatial information and class information. Second, our modified SMM is derived from the traditional finite mixture model (FMM) by adding the bias field correction model; the logarithmic likelihood function of traditional FMM is revised. Third, the noise and bias field can be easily extended to combine with the SMM model and EM algorithm. Last but not least, the exponential coefficients are employed to control the results of segmentation details. As a result, our effective and highly accurate method exhibits high robustness on both simulated and real MR image segmentation, compared to the state-of-the-art algorithms.