Ying Shihui, Wu Guorong, Liao Shu, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA ; Department of Mathematics, School of Science, Shanghai University, Shanghai 200444, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1030-1033. doi: 10.1109/ISBI.2013.6556653.
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.
在本文中，我们提出了一种新颖的组间图像配准方法，用于同时配准不同组的图像（例如，年轻人和老年人的大脑图像）。具体而言，我们使用分层二级图来对整个图像在流形上的分布进行建模，其中图内表示每组中的图像分布，而图间描述两组之间的关系。然后，将组间配准过程表述为图收缩的动态演化。我们方法的优点在于探索了整个图像分布的拓扑结构以指导图像配准。通过这种方式，每个图像在流形上与相邻图像协调，沿着图内同时优化的变形路径向总体中心变形。我们提出的方法还与其他当前最先进的组间配准方法进行了比较，在配准精度和鲁棒性方面，我们的方法取得了更好的配准结果。
