扩散张量磁共振图像分割研究进展

Progress in segmentation of diffusion tensor magnetic resonance images

随着扩散张量成像(diffusion tensor imaging,DTI)在临床的广泛应用,扩散张量分割方法已成为国内外医学图像处理与分析领域的研究热点之一.本文对近年来提出的各种扩散张量图像分割方法进行了综述,重点分析了以聚类、图割和水平集为基础的扩散张量分割方法的研究现状及其最新进展,分别讨论了各类方法中代表性算法的主要计算思路,并定性地分析、比较了这些算法的分割对象、所采用的相似性测度以及优缺点.最后,归纳总结了现有方法的主要特点,同时对扩散张量图像分割方法的未来发展方向进行了展望.

With the broad usage of diffusion tensor imaging （DTI） modality in clinical medical treatment, the DT image segmentation has become a research focus on medical image processing and analysis at home and abroad. In this paper, we reviewed various segmentation methods of DT images in recent years, and mainly investigated the state of the art and recent advances of those based on clustering, graph cuts and level set. Moreover, we discussed the computing procedure of each typical algorithm respectively, analyzed and compared segmentation objects, advantages, disadvantages and similarity metrics of these methods qualitatively. At the end, after summarizing main characteristics of existing methods, we prospected future development trend on DT image segmentation.