Groupwise registration based on hierarchical image clustering and atlas synthesis.
Groupwise registration based on hierarchical image clustering and atlas synthesis.
复制标题
DOI：
10.1002/hbm.20923
复制
发表时间：
2010-08
影响因子：
4.8
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Wang, Qian;Chen, Liya;Yap, Pew-Thian;Wu, Guorong;Shen, Dinggang
最近已经提出了分组配准，用于组中所有图像的同时和一致的配准。由于许多变形参数需要被优化为每个图像下注册，可以有效地处理由传统的成组注册方法的图像的数量是有限的。此外，由于受试者之间的显著差异，登记的稳健性岌岌可危。为了克服这些问题，我们提出了一个groupwise注册框架，这是基于一个层次化的图像聚类和图集合成策略。其基本思想是将一个大规模的分组注册问题分解为一系列小规模的问题，每个问题都相对容易使用通用计算机解决。特别是，我们采用了一种称为亲和传播的方法，这是专为快速和强大的聚类，分层聚类图像到金字塔的类。然后进行组内配准以配准各个类别内的所有图像，从而产生每个类别的代表性中心图像。这些不同类别的中心图像被进一步配准，从金字塔的底部到顶部。一旦配准到达金字塔的顶点，就合成单个中心图像或图谱。利用这种策略，我们可以高效和有效地注册一个大的图像组，构建他们的图集，并在同一时间，建立每个图像和图集之间的形状对应。我们已经评估了我们的框架使用真实的和模拟数据，结果表明，我们的框架实现了更好的鲁棒性和配准精度相比，传统的方法。
Groupwise registration has recently been proposed for simultaneous and consistent registration of all images in a group. Since many deformation parameters need to be optimized for each image under registration, the number of images that can be effectively handled by conventional groupwise registration methods is limited. Moreover, the robustness of registration is at stake due to significant intersubject variability. To overcome these problems, we present a groupwise registration framework, which is based on a hierarchical image clustering and atlas synthesis strategy. The basic idea is to decompose a large-scale groupwise registration problem into a series of small-scale problems, each of which is relatively easy to solve using a general computer. In particular, we employ a method called affinity propagation, which is designed for fast and robust clustering, to hierarchically cluster images into a pyramid of classes. Intraclass registration is then performed to register all images within individual classes, resulting in a representative center image for each class. These center images of different classes are further registered, from the bottom to the top in the pyramid. Once the registration reaches the summit of the pyramid, a single center image, or an atlas, is synthesized. Utilizing this strategy, we can efficiently and effectively register a large image group, construct their atlas, and, at the same time, establish shape correspondences between each image and the atlas. We have evaluated our framework using real and simulated data, and the results indicate that our framework achieves better robustness and registration accuracy compared to conventional methods.
登录
查看更多内容
影响因子：
10.9
作者：
Maintz, J B;Viergever, M A
通讯作者：
Viergever, M A
影响因子：
10.6
作者：
Holden, M;Hill, DLG;Hawkes, DJ
通讯作者：
Hawkes, DJ
影响因子：
10.6
作者：
Mattes, D;Haynor, DR;Eubank, W
通讯作者：
Eubank, W
影响因子：
2.3
作者：
Miller, M;Banerjee, A;Matejic, L
通讯作者：
Matejic, L
影响因子：
1.3
作者：
Davatzikos, C;Vaillant, M;Bryan, RN
通讯作者：
Bryan, RN