Topology adaptive deformable surfaces for medical image volume segmentation
Topology adaptive deformable surfaces for medical image volume segmentation
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DOI：
10.1109/42.811261
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发表时间：
1999-10-01
影响因子：
10.6
通讯作者：
Terzopoulos, D
中科院分区：
文献类型：
--
作者：
McInerney, T;Terzopoulos, D
可变形模型是一种基于模型的医学图像分析技术，它包括可变形轮廓（流行的蛇）和可变形表面。我们开发了一类新的可变形模型，制定可变形表面的仿射细胞图像分解（ACID）。我们的方法显着扩展了标准的可变形表面，同时保留其交互性和其他理想的属性。特别是，ACID诱导一个有效的reparameterization机制，使参数化变形表面演变成复杂的几何形状，甚至修改其拓扑结构的必要。我们证明，我们的新的ACID为基础的可变形表面，被称为T-表面，可以有效地分割复杂的解剖结构，从医学体积图像。
Deformable models, which include deformable contours (the popular snakes) and deformable Surfaces, are a powerful model-based medical image analysis technique. We develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). Our approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. We demonstrate that our new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.