Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review

被引:220
作者
Suri, JS [1 ]
Liu, KC
Singh, S
Laxminarayan, SN
Zeng, XL
Reden, L
机构
[1] Philips Med Syst Inc, MR Clin Sci Div, Cleveland, OH 44143 USA
[2] Univ Exeter, Dept Comp Sci, PANN, Exeter EX4 4PT, Devon, England
[3] New Jersey Inst Technol, Dept Biomed Engn, Newark, NJ 07102 USA
[4] R2 Technol Inc, Los Altos, CA 94022 USA
来源
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE | 2002年 / 6卷 / 01期
关键词
cortex; deformable models; differential geometry; front; fuzzy; level sets; propagation; regularization; segmentation; stopping forces; topology;
D O I
10.1109/4233.992158
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, this paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. This paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis.
引用
收藏
页码:8 / 28
页数:21
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