Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images

被引：69

作者：

Liew, AWC ^{[1
]}

Yan, H

机构：

[1] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China

[2] City Univ Hong Kong, Dept Comp Engn & Informat Technol, Kowloon, Hong Kong, Peoples R China

[3] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

来源：

CURRENT MEDICAL IMAGING | 2006年 / 2卷 / 01期

关键词：

medical imaging; magnetic resonance imaging; brain tissue segmentation; intensity nonuniformity artifact; partial volume artifact; fuzzy clustering; image segmentation;

D O I：

10.2174/157340506775541604

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classification-based, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy c-means clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.

引用

页码：91 / 103

页数：13

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