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Indirect Image Registration with Large Diffeomorphic Deformations
Chen, Chong1; Oktem, Ozan2
AbstractThis paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting, where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends to zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.
Keywordindirect image registration shape theory large deformations diffeomorphisms shape regularization image reconstruction tomography
Funding ProjectSwedish Foundation for Strategic Research[AM13-0049] ; National Natural Science Foundation of China (NSFC)[11301520]
WOS Research AreaComputer Science ; Mathematics ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Mathematics, Applied ; Imaging Science & Photographic Technology
WOS IDWOS:000428946200019
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, LSEC, ICMSEC, Beijing 100190, Peoples R China
2.KTH Royal Inst Technol, Dept Math, S-10044 Stockholm, Sweden
Recommended Citation
GB/T 7714
Chen, Chong,Oktem, Ozan. Indirect Image Registration with Large Diffeomorphic Deformations[J]. SIAM JOURNAL ON IMAGING SCIENCES,2018,11(1):575-617.
APA Chen, Chong,&Oktem, Ozan.(2018).Indirect Image Registration with Large Diffeomorphic Deformations.SIAM JOURNAL ON IMAGING SCIENCES,11(1),575-617.
MLA Chen, Chong,et al."Indirect Image Registration with Large Diffeomorphic Deformations".SIAM JOURNAL ON IMAGING SCIENCES 11.1(2018):575-617.
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