Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Sci Rep. 2019 Sep 3;9(1):12703. doi: 10.1038/s41598-019-48491-9.
Groupwise registration aligns a set of images to a common space. It can however be inefficient and ineffective when dealing with datasets with significant anatomical variations. To mitigate these problems, we propose a groupwise registration framework based on hierarchical multi-level and multi-resolution shrinkage of a graph set. First, to deal with datasets with complex inhomogeneous image distributions, we divide the images hierarchically into multiple clusters. Since the images in each cluster have similar appearances, they can be registered effectively. Second, we employ a multi-resolution strategy to reduce computational cost. Experimental results on two public datasets show that our proposed method yields state-of-the-art registration accuracy with significantly reduced computational time.
分组配准将一组图像对齐到公共空间。然而，当处理具有显著解剖变异的数据集时，它可能效率低下且效果不佳。为了解决这些问题，我们提出了一种基于图集分层多级和多分辨率收缩的分组配准框架。首先，为了处理具有复杂非均匀图像分布的数据集，我们将图像分层划分为多个聚类。由于每个聚类中的图像具有相似的外观，因此可以有效地对其进行配准。其次，我们采用多分辨率策略来降低计算成本。在两个公共数据集上的实验结果表明，我们提出的方法在显著减少计算时间的同时，达到了最先进的配准精度。
