Diffusion tensor image registration using hybrid connectivity and tensor features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Qian | - |
dc.contributor.author | Yap, Pew-Thian | - |
dc.contributor.author | Wu, Guorong | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-05T07:20:56Z | - |
dc.date.available | 2021-09-05T07:20:56Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-07 | - |
dc.identifier.issn | 1065-9471 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/98079 | - |
dc.description.abstract | Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Hum Brain Mapp 35:3529-3546, 2014. (c) 2013 Wiley Periodicals, Inc. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | GUIDED GROUPWISE REGISTRATION | - |
dc.subject | DEFORMABLE REGISTRATION | - |
dc.subject | SPATIAL NORMALIZATION | - |
dc.subject | NONRIGID REGISTRATION | - |
dc.subject | BRAIN IMAGES | - |
dc.subject | WHITE-MATTER | - |
dc.subject | MR-IMAGES | - |
dc.subject | ORIENTATION | - |
dc.subject | OPTIMIZATION | - |
dc.subject | INFORMATION | - |
dc.title | Diffusion tensor image registration using hybrid connectivity and tensor features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1002/hbm.22419 | - |
dc.identifier.scopusid | 2-s2.0-84902154609 | - |
dc.identifier.wosid | 000337746800049 | - |
dc.identifier.bibliographicCitation | HUMAN BRAIN MAPPING, v.35, no.7, pp.3529 - 3546 | - |
dc.relation.isPartOf | HUMAN BRAIN MAPPING | - |
dc.citation.title | HUMAN BRAIN MAPPING | - |
dc.citation.volume | 35 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 3529 | - |
dc.citation.endPage | 3546 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.relation.journalWebOfScienceCategory | Neuroimaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | GUIDED GROUPWISE REGISTRATION | - |
dc.subject.keywordPlus | DEFORMABLE REGISTRATION | - |
dc.subject.keywordPlus | SPATIAL NORMALIZATION | - |
dc.subject.keywordPlus | NONRIGID REGISTRATION | - |
dc.subject.keywordPlus | BRAIN IMAGES | - |
dc.subject.keywordPlus | WHITE-MATTER | - |
dc.subject.keywordPlus | MR-IMAGES | - |
dc.subject.keywordPlus | ORIENTATION | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordAuthor | diffusion tensor image registration | - |
dc.subject.keywordAuthor | connectivity features | - |
dc.subject.keywordAuthor | tensor features | - |
dc.subject.keywordAuthor | correspondence detection | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.