HYBRID DEFORMABLE MODEL FOR ANEURYSM SEGMENTATION

被引：15

作者：

Demirci, Stefanie ^{[1
]}

Lejeune, Guy ^{[1
]}

Navab, Nassir ^{[1
]}

机构：

[1] Tech Univ Munich, D-8000 Munich, Germany

来源：

2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2 | 2009年

关键词：

Image segmentation; abdominal aortic aneurysm; deformable model; NURBS;

D O I：

10.1109/ISBI.2009.5192976

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Automatic extraction of aortic aneurysm thrombus is a non-trivial challenge for existing segmentation algorithms. Due to similar intensity, the boundary to surrounding tissue is characterized by a small gradient. On the other hand, the aneurysm contains calcification spots that introduce wrong gradients. Therefore, purely intensity- or gradient-based methods fail to give optimal results. In this paper, we present a hybrid deformable model approach that integrates local and global image information and combines it with shape constraints. By the use of NURBS surfaces and distance functions, segmentation leakage into adjacent structures is prevented. The results of several experiments were evaluated by standard measures and expert inspection.

引用

页码：33 / 36

页数：4

共 7 条

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