Robust MR Image Segmentation Using the Trimmed Likelihood Estimator in Asymmetric Student's-t Mixture Model

Finite mixture model (FMM) has been widely used for unsupervised segmentation of magnetic resonance (MR) images in recent years. However, in real applications, the distribution of the observed data usually contains an unknown fraction of outliers, which would interfere with the estimation of the parameters of the mixture model. The statistical model-based technique which provides a theoretically well segmentation criterion in presence of outliers is the mixture modeling and the trimming approach. Therefore, in this paper, a robust estimation of asymmetric Student's-t mixture model (ASMM) using the trimmed likelihood estimator for MR image segmentation has been proposed. The proposed method is supposed to discard the outliers, and then to estimate the parameters of the ASMM with the remaining samples. The advantages of the proposed algorithm are that its robustness to dispose the disturbance of outliers and its flexibility to describe various shapes of data. Finally, expectation-maximization (EM) algorithm is adopted to maximize the log-likelihood and to obtain the estimation of the parameters. The experimental results show that the proposed method has a better performance on the segmentation of synthetic data and real MR images.