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INTER-GROUP IMAGE REGISTRATION BY HIERARCHICAL GRAPH SHRINKAGE

机译:通过分层图形收缩对组间图像进行注册

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摘要

In this paper, we propose a novel inter-group image registration method to register different groups of images (e.g., young and elderly brains) simultaneously. Specifically, we use a hierarchical two-level graph to model the distribution of entire images on the manifold, with intra-graph representing the image distribution in each group and the inter-graph describing the relationship between two groups. Then the procedure of inter-group registration is formulated as a dynamic evolution of graph shrinkage. The advantage of our method is that the topology of entire image distribution is explored to guide the image registration. In this way, each image coordinates with its neighboring images on the manifold to deform towards the population center, by following the deformation pathway simultaneously optimized within the graph. Our proposed method has been also compared with other state-of-the-art inter-group registration methods, where our method achieves better registration results in terms of registration accuracy and robustness.
机译:在本文中,我们提出了一种新颖的组间图像配准方法来同时配准不同组的图像(例如年轻人和老年人的大脑)。具体而言,我们使用分层两级图来建模整个图像在流形上的分布,其中内部图表示每个组中的图像分布,而内部图表示两个组之间的关系。然后将组间注册的过程表述为图收缩的动态演变。我们方法的优点是探索了整个图像分布的拓扑结构,以指导图像配准。以这种方式,通过遵循在图形内同时优化的变形路径,每个图像与其在歧管上的相邻图像协调以朝着人口中心变形。我们提出的方法也已与其他最新的组间注册方法进行了比较,后者在注册准确性和鲁棒性方面达到了更好的注册结果。

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