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Hierarchical image registration with an active contour-based atlas registration model

机译:基于主动轮廓的地图集配准模型进行分层图像配准

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This paper proposes to apply the non parametric atlas registration framework we have recently developed in [6]. This technique derived from the optical flow model and the active contour framework allows to base the registration of an anatomical atlas on selected structures. A well-suited application of our model is the non rigid registration of medical images based on a hierarchical atlas. This hierarchical registration approach that we have previously introduced in [7], aims to better exploit the spatial dependencies that exist between anatomical structures in an image matching process. Its basic idea is to first register the structures the most relevant to estimate the deformation in order to help the registration of secondary structures. This aims to reduce the risks of mismatching. Here, we propose to test our novel simultaneous registration and segmentation model on different types of medical image registration problems. Results show the advantages to combine our active contour-based registration framework with the structure-based hierarchical approach and highlight the importance of the registration order of the anatomical structures.
机译:本文建议应用我们最近在[6]中开发的非参数图集注册框架。从光流模型和活动轮廓框架派生的这项技术允许将解剖图谱的配准基于选定的结构。我们的模型的一个非常合适的应用是基于分层地图集的医学图像的非刚性配准。我们先前在文献[7]中引入的这种分层配准方法旨在更好地利用图像匹配过程中解剖结构之间存在的空间依赖性。它的基本思想是首先对最相关的结构进行配准以估计变形,以帮助辅助结构的配准。目的是减少不匹配的风险。在这里,我们建议针对不同类型的医学图像配准问题测试我们新颖的同时配准和分割模型。结果显示了将我们的基于主动轮廓的配准框架与基于结构的分层方法相结合的优势,并突出了解剖结构的配准顺序的重要性。

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