Depolarization metric spaces for biological tissues classification

Classification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided-recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework...

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Detalles Bibliográficos
Autores: Van Eeckhout, Albert, García-Caurel, Enric, Ossikovski, Razvigor, Lizana, Angel, Rodríguez, Carla, González-Arnay, Emilio, Campos, Juan
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/694059
Acceso en línea:http://hdl.handle.net/10486/694059
https://dx.doi.org/10.1002/jbio.202000083
Access Level:acceso abierto
Palabra clave:biological tissue
biomedical
depolarization
imaging
Mueller matrix
polarimetry
Medicina
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spelling Depolarization metric spaces for biological tissues classificationVan Eeckhout, AlbertGarcía-Caurel, EnricOssikovski, RazvigorLizana, AngelRodríguez, CarlaGonzález-Arnay, EmilioCampos, Juanbiological tissuebiomedicaldepolarizationimagingMueller matrixpolarimetryMedicinaClassification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided-recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework, we study the potential of some polarimetric metrics, the so-called depolarization spaces, for the classification of biological tissues. The analysis is performed using 120 biological ex vivo samples of three different tissues types. Based on these data collection, we provide for the first time a comparison between these depolarization spaces, as well as with most commonly used depolarization metrics, in terms of biological samples discrimination. The results illustrate the way to determine the set of depolarization metrics which optimizes tissue classification efficiencies. In that sense, the results show the interest of the method which is general, and which can be applied to study multiple types of biological samples, including of course human tissues. The latter can be useful for instance, to improve and to boost applications related to optical biopsy.Agència de Gestió d'Ajuts Universitaris i de Recerca, Grant/Award Number: 2017-SGR-001500; Ministerio de Economía y Competitividad, Grant/Award Numbers: Fondos FEDER, RTI2018-097107-B-C31WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimDepartamento de Anatomía, Histología y NeurocienciaFacultad de Medicina20202020-08-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/694059https://dx.doi.org/10.1002/jbio.202000083reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6940592026-06-23T12:46:27Z
dc.title.none.fl_str_mv Depolarization metric spaces for biological tissues classification
title Depolarization metric spaces for biological tissues classification
spellingShingle Depolarization metric spaces for biological tissues classification
Van Eeckhout, Albert
biological tissue
biomedical
depolarization
imaging
Mueller matrix
polarimetry
Medicina
title_short Depolarization metric spaces for biological tissues classification
title_full Depolarization metric spaces for biological tissues classification
title_fullStr Depolarization metric spaces for biological tissues classification
title_full_unstemmed Depolarization metric spaces for biological tissues classification
title_sort Depolarization metric spaces for biological tissues classification
dc.creator.none.fl_str_mv Van Eeckhout, Albert
García-Caurel, Enric
Ossikovski, Razvigor
Lizana, Angel
Rodríguez, Carla
González-Arnay, Emilio
Campos, Juan
author Van Eeckhout, Albert
author_facet Van Eeckhout, Albert
García-Caurel, Enric
Ossikovski, Razvigor
Lizana, Angel
Rodríguez, Carla
González-Arnay, Emilio
Campos, Juan
author_role author
author2 García-Caurel, Enric
Ossikovski, Razvigor
Lizana, Angel
Rodríguez, Carla
González-Arnay, Emilio
Campos, Juan
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Anatomía, Histología y Neurociencia
Facultad de Medicina
dc.subject.none.fl_str_mv biological tissue
biomedical
depolarization
imaging
Mueller matrix
polarimetry
Medicina
topic biological tissue
biomedical
depolarization
imaging
Mueller matrix
polarimetry
Medicina
description Classification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided-recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework, we study the potential of some polarimetric metrics, the so-called depolarization spaces, for the classification of biological tissues. The analysis is performed using 120 biological ex vivo samples of three different tissues types. Based on these data collection, we provide for the first time a comparison between these depolarization spaces, as well as with most commonly used depolarization metrics, in terms of biological samples discrimination. The results illustrate the way to determine the set of depolarization metrics which optimizes tissue classification efficiencies. In that sense, the results show the interest of the method which is general, and which can be applied to study multiple types of biological samples, including of course human tissues. The latter can be useful for instance, to improve and to boost applications related to optical biopsy.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-08-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
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 http://hdl.handle.net/10486/694059
https://dx.doi.org/10.1002/jbio.202000083
url http://hdl.handle.net/10486/694059
https://dx.doi.org/10.1002/jbio.202000083
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
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
publisher.none.fl_str_mv WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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