Topology-preserving tissue classification of magnetic resonance brain images
Topology-preserving tissue classification of magnetic resonance brain images
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DOI：
10.1109/tmi.2007.893283
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发表时间：
2007-04-01
影响因子：
10.6
通讯作者：
Pham, Dzung L.
中科院分区：
文献类型：
--
作者：
Bazin, Pierre-Louis;Pham, Dzung L.
本文提出了一种新的医学图像多目标分割框架，该框架考虑了模板给出的结构的拓扑性质和关系。这项技术被称为拓扑保持，解剖驱动分割(TOADS)，结合了统计组织分类、拓扑保持快速行进方法和图像配准的优点，以增强对象级别的关系，而对几何约束很少。当应用于大脑分割问题时，它在分割大脑主要结构的同时，直接提供了一个具有球状拓扑结构的皮质表面。在模拟和真实图像上的验证表征了该算法在噪声、不均匀和解剖变化方面的性能。
This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.