Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
Comput Med Imaging Graph. 2012 Mar;36(2):139-51. doi: 10.1016/j.compmedimag.2011.09.001. Epub 2011 Oct 19.
In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration.
本文提出了一种新的图像配准方法，通过使用一组适当的中间模板，实现了具有大形状差异的图像之间的精确配准。我们首先证明了方向性是图像对配准和组配准的关键因素，本文中定义方向性来描述配准方向和路径对配准性能的影响。在我们的解决方案中，中间模板选择和中间模板引导的配准是两个具有方向性的连贯步骤。为了利用方向性，基于所有有序图像对在图像群体中的不对称距离构建有向图，这与以前所有方法中基于对称距离度量的无向图有根本的不同，并且计算模板和主体之间的有向图上的最短距离。因此，可以通过在有向路径上依次通过中间模板对主体进行逐个注册，来更好地引导配准。所提出的基于有向图的解决方案也可用于组配准。具体来说，通过在有向图上构建最小生成树 (MSA)，同时确定群体中心（即选择的模板）以及从所有其余图像到群体中心的有向注册路径。基于有向图的配准算法的性能通过对合成数据集和真实脑磁共振图像的空间归一化进行了演示。结果表明，与基于无向图的方法和直接的成对配准相比，我们的方法可以获得更精确的配准结果。
