Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images

[EN] This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain...

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Detalles Bibliográficos
Autores: Aragonés Lozano, Mario|||0000-0002-8278-3947, Romero Martínez, José Oscar|||0000-0003-4081-9005, León Fernández, Antonio|||0000-0002-9374-9277
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::858408776aaa947a3554be07e94e809b
Acceso en línea:https://riunet.upv.es/handle/10251/235914
Access Level:acceso abierto
Palabra clave:Structured 3D-SVD
Tensor decomposition
Volumetric imaging
Progressive reconstruction
Biological imaging
Tensor compression
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spelling Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric ImagesAragonés Lozano, Mario|||0000-0002-8278-3947Romero Martínez, José Oscar|||0000-0003-4081-9005León Fernández, Antonio|||0000-0002-9374-9277Structured 3D-SVDTensor decompositionVolumetric imagingProgressive reconstructionBiological imagingTensor compression[EN] This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coefficients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.MDPI AGEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de ComunicacionesInstituto de Investigación para la Gestión Integrada de Zonas CosterasGrupo de Sistemas y Aplicaciones de Tiempo Real Distribuido. SATRDUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-04-16journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/235914reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::858408776aaa947a3554be07e94e809b2026-06-13T07:49:27Z
dc.title.none.fl_str_mv Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
title Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
spellingShingle Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
Aragonés Lozano, Mario|||0000-0002-8278-3947
Structured 3D-SVD
Tensor decomposition
Volumetric imaging
Progressive reconstruction
Biological imaging
Tensor compression
title_short Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
title_full Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
title_fullStr Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
title_full_unstemmed Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
title_sort Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
dc.creator.none.fl_str_mv Aragonés Lozano, Mario|||0000-0002-8278-3947
Romero Martínez, José Oscar|||0000-0003-4081-9005
León Fernández, Antonio|||0000-0002-9374-9277
author Aragonés Lozano, Mario|||0000-0002-8278-3947
author_facet Aragonés Lozano, Mario|||0000-0002-8278-3947
Romero Martínez, José Oscar|||0000-0003-4081-9005
León Fernández, Antonio|||0000-0002-9374-9277
author_role author
author2 Romero Martínez, José Oscar|||0000-0003-4081-9005
León Fernández, Antonio|||0000-0002-9374-9277
author2_role author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Comunicaciones
Instituto de Investigación para la Gestión Integrada de Zonas Costeras
Grupo de Sistemas y Aplicaciones de Tiempo Real Distribuido. SATRD
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Structured 3D-SVD
Tensor decomposition
Volumetric imaging
Progressive reconstruction
Biological imaging
Tensor compression
topic Structured 3D-SVD
Tensor decomposition
Volumetric imaging
Progressive reconstruction
Biological imaging
Tensor compression
description [EN] This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coefficients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-04-16
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/235914
url https://riunet.upv.es/handle/10251/235914
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406583358750720
score 15,812429

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