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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
2015-11-01
发表期刊INTERNATIONAL JOURNAL OF COMPUTER VISION
卷号115期号:2页码:69-86
文章类型Article
摘要Large 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.
关键词Computational Anatomy Diffeomorphic Metric Mapping Stationary Parameterization Landmark Matching Metric Approximation
WOS标题词Science & Technology ; Technology
DOI10.1007/s11263-015-0802-4
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; COMPUTATIONAL ANATOMY ; IMAGE REGISTRATION ; SUBGROUPS ; BRAIN ; STATISTICS ; FRAMEWORK ; FLOWS
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000362285700001
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10028
专题脑网络组研究中心
作者单位1.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
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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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