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Active Contours Under Topology Control—Genus Preserving Level Sets

机译:拓扑控制下的活动轮廓-保留类集

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摘要

We present a novel framework to exert topology control over a level set evolution. Level set methods offer several advantages over parametric active contours, in particular automated topological changes. In some applications, where some a priori knowledge of the target topology is available, topological changes may not be desirable. This is typically the case in biomedical image segmentation, where the topology of the target shape is prescribed by anatomical knowledge. However, topologically constrained evolutions often generate topological barriers that lead to large geometric inconsistencies. We introduce a topologically controlled level set framework that greatly alleviates this problem. Unlike existing work, our method allows connected components to merge, split or vanish under some specific conditions that ensure that the genus of the initial active contour (i.e. its number of handles) is preserved. We demonstrate the strength of our method on a wide range of numerical experiments and illustrate its performance on the segmentation of cortical surfaces and blood vessels.
机译:我们提出了一种新颖的框架,可对级别集的演变进行拓扑控制。水平集方法相对于参数化活动轮廓具有多个优点,特别是自动拓扑更改。在某些应用中,如果可以获得目标拓扑的一些先验知识,则可能不需要拓扑更改。在生物医学图像分割中通常是这种情况,其中目标形状的拓扑由解剖知识规定。但是,受拓扑约束的演化通常会生成拓扑障碍,从而导致较大的几何不一致。我们介绍了一种拓扑控制的水平集框架,该框架可以极大地缓解此问题。与现有工作不同,我们的方法允许连接的组件在某些特定条件下合并,分裂或消失,以确保保留初始活动轮廓的属类(即其手柄数量)。我们在广泛的数值实验中证明了我们方法的优势,并说明了其在皮层表面和血管分割方面的性能。

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