Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization

	Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization
	Yang, Xianfeng 1,2; Li, Yonghui 1; Reutens, David 2; Jiang, Tianzi1,2,3,4,5
刊名	INTERNATIONAL JOURNAL OF COMPUTER VISION
	2015-11-01
卷号	115 期号:2 页码:69-86
关键词	Computational anatomy Diffeomorphic metric mapping Stationary parameterization Landmark matching Metric approximation
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
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记录号	WOS:000362285700001
公开日期	2015-12-24
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/10028]
专题	自动化研究所_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
推荐引用方式 GB/T 7714	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.