TY - GEN
T1 - Directed graph based image registration
AU - Jia, Hongjun
AU - Wu, Guorong
AU - Wang, Qian
AU - Wang, Yaping
AU - Kim, Minjeong
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants EB006733, EB008374, MH088520 and EB009634.
PY - 2011
Y1 - 2011
N2 - In this paper, a novel intermediate templates guided image registration algorithm is proposed to achieve accurate registration results with a more appropriate strategy for intermediate template selection. We first demonstrate that registration directions and paths play a key role in the intermediate template guided registration methods. In light of this, a directed graph is built based on the asymmetric distances defined on all ordered image-pairs in the dataset. The allocated directed path can be used to guide the pairwise registration by successively registering the underlying subject towards the template through all intermediate templates on the path. Moreover, for the groupwise registration, a minimum spanning arborescence (MSA) is built with both the template (the root) and the directed paths (from all images to the template) determined simultaneously. Experiments on synthetic and real datasets show that our method can achieve more accurate registration results than both the traditional pairwise registration and the undirected graph based registration methods.
AB - In this paper, a novel intermediate templates guided image registration algorithm is proposed to achieve accurate registration results with a more appropriate strategy for intermediate template selection. We first demonstrate that registration directions and paths play a key role in the intermediate template guided registration methods. In light of this, a directed graph is built based on the asymmetric distances defined on all ordered image-pairs in the dataset. The allocated directed path can be used to guide the pairwise registration by successively registering the underlying subject towards the template through all intermediate templates on the path. Moreover, for the groupwise registration, a minimum spanning arborescence (MSA) is built with both the template (the root) and the directed paths (from all images to the template) determined simultaneously. Experiments on synthetic and real datasets show that our method can achieve more accurate registration results than both the traditional pairwise registration and the undirected graph based registration methods.
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U2 - 10.1007/978-3-642-24319-6_22
DO - 10.1007/978-3-642-24319-6_22
M3 - Conference contribution
AN - SCOPUS:80053999033
SN - 9783642243189
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 183
BT - Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
T2 - 2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 18 September 2011
ER -