Robust Mixture-Parameter Estimation for Unsupervised Segmentation of Brain MR Images

Abstract

Methods for automated segmentation of brain MR images are routinely used in large-scale neurological studies. Automated segmentation is usually performed by unsupervised methods, since these can be used even if different MR sequences or different pathologies are studied. The unsupervised methods model intensity distribution of major brain structures using mixture models, the parameters of which need to be robustly estimated from MR data and in presence of outliers. In this paper, we propose a robust mixture-parameter estimation that detects outliers as samples with low significance level of the corresponding mixture component and iteratively re-estimates the fraction of outliers. Results on synthetic and real brain image datasets demonstrate superior robustness of the proposed method as compared to the popular FAST-TLE method over a broad range of trimming fraction values. The latter is important for segmenting brain structures with pathology, the extent of which is hard to predict in large-scale imaging studies.