University of Manchester, UK.
IEEE Trans Pattern Anal Mach Intell. 2010 Nov;32(11):1994-2005. doi: 10.1109/TPAMI.2009.193.
Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically, they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field, and optimization methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here, we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance.
分组图像配准算法旨在建立图像集之间的密集对应关系。通常，它们涉及迭代改进每个图像与不断演变的平均值之间的配准。已经提出了多种方法，它们在目标函数的选择、变形场的表示和优化方法方面有所不同。考虑到任务的复杂性，最终的准确性受到为每个组件做出的选择的显著影响。在这里，我们提出了一种分组配准算法，它可以利用图像强度的统计信息和组内形状的范围来实现精确匹配。通过对大量图像（二维和三维）进行测试，我们探讨了使用不同图像表示和不同统计形状约束的效果。我们证明了对这些表示形式的仔细选择可以显著提高整体性能。
