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Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization
Yang, Xianfeng1,2; Li, Yonghui1; Reutens, David2; Jiang, Tianzi1,2,3,4,5
Source PublicationINTERNATIONAL JOURNAL OF COMPUTER VISION
2015-11-01
Volume115Issue:2Pages:69-86
SubtypeArticle
AbstractLarge deformation diffeomorphic metric mapping (LDDMM) has been shown as an effective computational paradigm to measure anatomical variability. However, its time-varying vector field parameterization of diffeomorphism flow leads to computationally expensive implementation, as well as some theoretical issues in metric based shape analysis, e.g. high order metric approximation via Baker-Campbell-Hausdorff (BCH) formula. To address these problems, we study the role of stationary vector field parameterization in context of LDDMM. Under this setting registration is formulated as finding the Lie group exponential path with minimal energy in Riemannian manifold of diffeomorphisms bringing two shapes together. Accurate derivation of Euler-Lagrange equation shows that optimal vector field for landmark matching is associated with singular momenta at landmark trajectories in whole time domain, and a new momentum optimization scheme is proposed to solve the variational problem. Length of group exponential path is also proposed as an alternative shape metric to geodesic distance, and pair-wise metrics among a population are computed through an approximation method via BCH formula which only needs registrations to a template. The proposed methods have been tested on both synthesized data and real database. Compared to non-stationary parameterization, this method can achieve comparable registration accuracy in significantly reduced time. Second order metric approximation by this method also improves significantly over first order, which can not be achieved by non-stationary parameterization. Correlation between the two shape metrics is also investigated, and their statistical power in clinical study compared.
KeywordComputational Anatomy Diffeomorphic Metric Mapping Stationary Parameterization Landmark Matching Metric Approximation
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s11263-015-0802-4
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; COMPUTATIONAL ANATOMY ; IMAGE REGISTRATION ; SUBGROUPS ; BRAIN ; STATISTICS ; FRAMEWORK ; FLOWS
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000362285700001
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10028
Collection脑网络组研究中心
Affiliation1.Univ Queensland, Queensland Brain Inst, Brisbane, Qld 4072, Australia
2.Univ Queensland, Ctr Adv Imaging, Brisbane, Qld 4072, Australia
3.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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Yang, Xianfeng,Li, Yonghui,Reutens, David,et al. Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2015,115(2):69-86.
APA Yang, Xianfeng,Li, Yonghui,Reutens, David,&Jiang, Tianzi.(2015).Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization.INTERNATIONAL JOURNAL OF COMPUTER VISION,115(2),69-86.
MLA Yang, Xianfeng,et al."Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization".INTERNATIONAL JOURNAL OF COMPUTER VISION 115.2(2015):69-86.
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