Groupwise Registration by Hierarchical Anatomical Correspondence Detection

Groupwise registration has been widely investigated in recent years due to its importance in analyzing population data in many clinical applications. To our best knowledge, most of the groupwise registration algorithms only utilize the intensity information. However, it is well known that using intensity only is not sufficient to achieve the anatomically sound correspondences in medical image registration. In this paper, we propose a novel feature-based groupwise registration algorithm to address the anatomical correspondence across subjects, by using the attribute vector as the morphological signature on each voxel. Similar with most of state-of-the-art groupwise registration algorithms which simultaneously estimate the transformation fields for all subjects, we develop an energy function to minimize the inter-subject discrepancies on anatomical structures and drive all subjects towards the hidden common space. To make the algorithm efficient and robust, we decouple the complex group...

• Type: Journal
• Year: 2010
• Where: MICCAI
• Authors: Guorong Wu, Qian Wang, Hongjun Jia, Dinggang Shen
• Keywords: Algorithm, Groupwise Registration, Medical Imaging, MICCAI 2010, Voxels