Twining Carole J, Cootes Tim, Marsland Stephen, Petrovic Vladimir, Schestowitz Roy, Taylor Chris J
Imaging Science and Biomedical Engineering (ISBE), Stopford Building, University of Manchester, Manchester, UK.
Inf Process Med Imaging. 2005;19:1-14. doi: 10.1007/11505730_1.
The non-rigid registration of a group of images shares a common feature with building a model of a group of images: a dense, consistent correspondence across the group. Image registration aims to find the correspondence, while modelling requires it. This paper presents the theoretical framework required to unify these two areas, providing a groupwise registration algorithm, where the inherently groupwise model of the image data becomes an integral part of the registration process. The performance of this algorithm is evaluated by extending the concepts of generalisability and specificity from shape models to image models. This provides an independent metric for comparing registration algorithms of groups of images. Experimental results on MR data of brains for various pairwise and groupwise registration algorithms is presented, and demonstrates the feasibility of the combined registration/modelling framework, as well as providing quantitative evidence for the superiority of groupwise approaches to registration.
该组内存在密集、一致的对应关系。图像配准旨在找到这种对应关系，而建模则需要这种对应关系。本文提出了统一这两个领域所需的理论框架，提供了一种组内配准算法，其中图像数据固有的组内模型成为配准过程的一个组成部分。通过将泛化性和特异性的概念从形状模型扩展到图像模型来评估该算法的性能。这为比较图像组的配准算法提供了一个独立的指标。给出了针对各种成对和组内配准算法的脑部磁共振数据的实验结果，证明了组合配准/建模框架的可行性，并为组内配准方法的优越性提供了定量证据。
