MR brain image analysis by distribution learning and relaxation labeling

Abstract

This paper addresses the quantification and segmentation in brain tissue analysis by using MR brain scan. It is shown that this problem can be solved by distribution learning and relaxation labeling, an efficient method that may be particularly useful in quantifying and segmenting abnormal brain cases where the distribution of each tissue type may heavily overlap. The new technique utilizes suitable statistical models for both pixel and context images. The analysis is then formulated as an optimization problem of model-histogram fitting and global consistency labeling. The quantification is solved by a probabilistic self-organizing map, and the segmentation is performed through local Bayesian decisions. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms conventional classification and Bayesian based approaches.