Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/58308
Campo DC Valoridioma
dc.contributor.authorHernández Guedes, Abiánen_US
dc.contributor.authorArteaga-Marrero, Nataliaen_US
dc.contributor.authorVilla, Enriqueen_US
dc.contributor.authorFabelo Gómez, Himar Antonioen_US
dc.contributor.authorMarrero Callicó, Gustavo Ivánen_US
dc.contributor.authorRuiz Alzola, Juan Bautistaen_US
dc.date.accessioned2019-12-10T17:44:28Z-
dc.date.available2019-12-10T17:44:28Z-
dc.date.issued2019en_US
dc.identifier.isbn978-3-030-30644-1en_US
dc.identifier.issn0302-9743en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/58308-
dc.description.abstractTemperature data acquired by infrared sensors provide relevant information to assess different medical pathologies in early stages, when the symptoms of the diseases are not visible yet to the naked eye. Currently, a clinical system that exploits the use of multimodal images (visible, depth and thermal infrared) is being developed for diabetic foot monitoring. The workflow required to analyze these images starts with their acquisition and the automatic feet segmentation. A novel approach is presented for automatic feet segmentation using Deep Learning employing an architecture composed of an encoder and decoder (U-Net architecture) and applying a segmentation of planes in point cloud data, using the depth information of pixels labeled in the neural network prediction. The proposed automatic segmentation is a robust method for this case study, providing results in a short time and achieving better performance than other traditional segmentation methods as well as a basic U-Net segmentation system.en_US
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceImage Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science, v. 11752 LNCS, p. 414-424en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherRGB-D imagesen_US
dc.subject.otherMultimodal imagesen_US
dc.subject.otherDeep Learningen_US
dc.subject.otherAutomatic segmentationen_US
dc.titleAutomatic segmentation based on deep learning techniques for diabetic foot monitoring through multimodal imagesen_US
dc.typeinfo:eu-repo/semantics/bookParten_US
dc.typeBook parten_US
dc.relation.conference20th International Conference on Image Analysis and Processing, (ICIAP 2019)-
dc.identifier.doi10.1007/978-3-030-30645-8_38en_US
dc.identifier.scopus85072887186-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid57203173306-
dc.contributor.authorscopusid14038607600-
dc.contributor.authorscopusid26325126700-
dc.contributor.authorscopusid56405568500-
dc.contributor.authorscopusid56006321500-
dc.contributor.authorscopusid56614041800-
dc.description.lastpage424en_US
dc.description.firstpage414en_US
dc.relation.volume11752 LNCSen_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Capítulo de libroen_US
dc.description.notasLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.utils.revisionen_US
dc.identifier.supplement0302-9743-
dc.identifier.supplement0302-9743-
dc.identifier.supplement0302-9743-
dc.identifier.conferenceidevents121666-
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.contributor.buulpgcBU-INFen_US
dc.contributor.buulpgcBU-INFen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,427
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dc.description.spiqQ1
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.event.eventsstartdate09-09-2019-
crisitem.event.eventsenddate13-09-2019-
crisitem.author.deptGIR IUIBS: Tecnología Médica y Audiovisual-
crisitem.author.deptIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.deptGIR IUIBS: Tecnología Médica y Audiovisual-
crisitem.author.deptIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-2508-2845-
crisitem.author.orcid0000-0002-9794-490X-
crisitem.author.orcid0000-0002-3784-5504-
crisitem.author.orcid0000-0002-3545-2328-
crisitem.author.parentorgIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Investigaciones Biomédicas y Sanitarias-
crisitem.author.fullNameHernández Guedes, Abián-
crisitem.author.fullNameFabelo Gómez, Himar Antonio-
crisitem.author.fullNameMarrero Callicó, Gustavo Iván-
crisitem.author.fullNameRuiz Alzola, Juan Bautista-
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