DocumentCode :
3333206
Title :
Groupwise Registration via Graph Shrinkage on the Image Manifold
Author :
Shihui Ying ; Guorong Wu ; Qian Wang ; Dinggang Shen
Author_Institution :
Dept. of Radiol., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
2323
Lastpage :
2330
Abstract :
Recently, group wise registration has been investigated for simultaneous alignment of all images without selecting any individual image as the template, thus avoiding the potential bias in image registration. However, none of current group wise registration method fully utilizes the image distribution to guide the registration. Thus, the registration performance usually suffers from large inter-subject variations across individual images. To solve this issue, we propose a novel group wise registration algorithm for large population dataset, guided by the image distribution on the manifold. Specifically, we first 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 group wise 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 group wise registration method on both synthetic and real datasets, with comparison to the two state-of-the-art group wise registration methods. All experimental results show that our proposed method achieves the best performance in terms of registration accuracy and robustness.
Keywords :
differential geometry; graph theory; image registration; dynamic shrinking; geodesic pathway; graph edges; graph nodes; graph shrinkage; groupwise registration algorithm; image alignment; image data distribution modeling; image distribution topology; image manifold; image registration; image warping; inter-subject variations; population dataset; real datasets; registration error; registration performance; synthetic datasets; Image edge detection; Manifolds; Registers; Sociology; Statistics; Topology; Vectors; Unbiased groupwise registration; diffeomorphism; graph shrinking; image manifold;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
ISSN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2013.301
Filename :
6619145
Link To Document :
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