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Nonlinear image registration with bidirectional metric and reciprocal regularization
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322897/ https://www.ncbi.nlm.nih.gov/pubmed/28231342 http://dx.doi.org/10.1371/journal.pone.0172432 |
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author | Ying, Shihui Li, Dan Xiao, Bin Peng, Yaxin Du, Shaoyi Xu, Meifeng |
author_facet | Ying, Shihui Li, Dan Xiao, Bin Peng, Yaxin Du, Shaoyi Xu, Meifeng |
author_sort | Ying, Shihui |
collection | PubMed |
description | Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. |
format | Online Article Text |
id | pubmed-5322897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53228972017-03-09 Nonlinear image registration with bidirectional metric and reciprocal regularization Ying, Shihui Li, Dan Xiao, Bin Peng, Yaxin Du, Shaoyi Xu, Meifeng PLoS One Research Article Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. Public Library of Science 2017-02-23 /pmc/articles/PMC5322897/ /pubmed/28231342 http://dx.doi.org/10.1371/journal.pone.0172432 Text en © 2017 Ying et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ying, Shihui Li, Dan Xiao, Bin Peng, Yaxin Du, Shaoyi Xu, Meifeng Nonlinear image registration with bidirectional metric and reciprocal regularization |
title | Nonlinear image registration with bidirectional metric and reciprocal regularization |
title_full | Nonlinear image registration with bidirectional metric and reciprocal regularization |
title_fullStr | Nonlinear image registration with bidirectional metric and reciprocal regularization |
title_full_unstemmed | Nonlinear image registration with bidirectional metric and reciprocal regularization |
title_short | Nonlinear image registration with bidirectional metric and reciprocal regularization |
title_sort | nonlinear image registration with bidirectional metric and reciprocal regularization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322897/ https://www.ncbi.nlm.nih.gov/pubmed/28231342 http://dx.doi.org/10.1371/journal.pone.0172432 |
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