Imaging Sciences Research Group, School of Cancer and Enabling Sciences, The University of Manchester, M13 9PT Manchester, U.K.
IEEE Trans Med Imaging. 2012 Feb;31(2):341-58. doi: 10.1109/TMI.2011.2169077. Epub 2011 Sep 23.
Groupwise nonrigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimization problem, a good initialization is required. Although the standard initialization-affine transformation-generally works well, it is often inadequate when registering images of complex structures. In this paper we present a more sophisticated method that uses the sparse matches of a parts+geometry model as the initialization. We show that both the model and its matches can be automatically obtained, and that the matches are able to effectively initialize a groupwise nonrigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the approach on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance.
分组非刚性图像配准是一种强大的工具，可用于自动建立图像集之间的对应关系。这些对应关系广泛用于构建形状和外观的统计模型。由于现有技术通常将配准视为优化问题，因此需要良好的初始化。尽管标准的初始化仿射变换通常效果很好，但在注册复杂结构的图像时，它往往不够充分。在本文中，我们提出了一种更复杂的方法，该方法使用零件+几何模型的稀疏匹配作为初始化。我们表明，可以自动获得模型及其匹配，并且匹配能够有效地初始化分组非刚性配准算法，从而得到准确的密集对应关系。我们还表明，在分组配准过程中构建的密集网格模型可用于准确地注释新图像。我们在三个难度逐渐增加的数据集上证明了该方法的有效性，并报告了对其性能的详细定量评估。
