期刊
NEUROIMAGE
卷 84, 期 -, 页码 626-638出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2013.09.023
关键词
Unbiased groupwise registration; Graph shrinking; Image manifold; Diffeomorphism
资金
- NIH [EB006733, EB008374, EB009634, AG041721]
- National Natural Science Foundation of China [61005002, 11101260]
- Ph.D. Programs Foundation of Ministry of Education of China [20103108120001]
- Discipline Project at the Corresponding Level of Shanghai [A.13-0101-12-005]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB006733, R01EB008374, R01EB009634] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH100217] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [R01AG042599, R01AG041721] Funding Source: NIH RePORTER
Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness. (C) 2013 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据