Diffusion maps for multimodal registration
Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computat...
| Autor: | |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2014 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/25895 |
| Acesso em linha: | http://hdl.handle.net/10230/25895 http://dx.doi.org/10.3390/s140610562 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Diffusion maps Spectral geometry Diffusion distance Multimodal registration |
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Diffusion maps for multimodal registrationPiella Fenoy, GemmaDiffusion mapsSpectral geometryDiffusion distanceMultimodal registrationMultimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (under project TIN2012-35874).MDPI201620162014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/25895http://dx.doi.org/10.3390/s140610562reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésSensors. 2014;14(6):10563-10577.info:eu-repo/grantAgreement/ES/3PN/TIN2012-35874© 2014 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article/ndistributed under the terms and conditions of the Creative Commons Attribution license/n(http://creativecommons.org/licenses/by/3.0/).http://creativecommons.org/licenses/by/3.0info:eu-repo/semantics/openAccessoai:recercat.cat:10230/258952026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Diffusion maps for multimodal registration |
| title |
Diffusion maps for multimodal registration |
| spellingShingle |
Diffusion maps for multimodal registration Piella Fenoy, Gemma Diffusion maps Spectral geometry Diffusion distance Multimodal registration |
| title_short |
Diffusion maps for multimodal registration |
| title_full |
Diffusion maps for multimodal registration |
| title_fullStr |
Diffusion maps for multimodal registration |
| title_full_unstemmed |
Diffusion maps for multimodal registration |
| title_sort |
Diffusion maps for multimodal registration |
| dc.creator.none.fl_str_mv |
Piella Fenoy, Gemma |
| author |
Piella Fenoy, Gemma |
| author_facet |
Piella Fenoy, Gemma |
| author_role |
author |
| dc.subject.none.fl_str_mv |
Diffusion maps Spectral geometry Diffusion distance Multimodal registration |
| topic |
Diffusion maps Spectral geometry Diffusion distance Multimodal registration |
| description |
Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information. |
| publishDate |
2014 |
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2014 2016 2016 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10230/25895 http://dx.doi.org/10.3390/s140610562 |
| url |
http://hdl.handle.net/10230/25895 http://dx.doi.org/10.3390/s140610562 |
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Inglés |
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Inglés |
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Sensors. 2014;14(6):10563-10577. info:eu-repo/grantAgreement/ES/3PN/TIN2012-35874 |
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http://creativecommons.org/licenses/by/3.0 info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/3.0 |
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openAccess |
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application/pdf application/pdf |
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MDPI |
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MDPI |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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