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dc.contributor.authorNavarro, Fulgencio
dc.contributor.authorEscudero Viñolo, Marcos 
dc.contributor.authorBescos Cano, Jesús 
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.contributor.otherUAM. Departamento de Tecnología Electrónica y de las Comunicacioneses_ES
dc.date.accessioned2021-04-26T15:53:31Z
dc.date.available2021-04-26T15:53:31Z
dc.date.issued2019-03-06
dc.identifier.citationIEEE Journal of Biomedical and Health Informatics 23.2 (2019): 501 – 508es_ES
dc.identifier.issn2168-2194 (print)es_ES
dc.identifier.issn2168-2208 (online)es_ES
dc.identifier.urihttp://hdl.handle.net/10486/694918
dc.description.abstractSkin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutiones_ES
dc.description.sponsorshipThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid)es_ES
dc.format.extent9 pag.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.ispartofIEEE Journal of Biomedical and Health Informaticses_ES
dc.rights© 2018 IEEEes_ES
dc.subject.othercomputer-aided diagnosis (CAD)es_ES
dc.subject.otherimage registrationes_ES
dc.subject.otherlesion evolution featurees_ES
dc.subject.otherLesion segmentationes_ES
dc.subject.otherLF-SLICes_ES
dc.subject.otherlocal featureses_ES
dc.subject.otherSP-SIFTes_ES
dc.subject.othersuperpixelses_ES
dc.titleAccurate segmentation and registration of skin lesion images to evaluate lesion changees_ES
dc.typearticlees_ES
dc.subject.ecienciaInformáticaes_ES
dc.subject.ecienciaMedicinaes_ES
dc.relation.publisherversionhttps://doi.org/10.1109/JBHI.2018.2825251es_ES
dc.identifier.doi10.1109/JBHI.2018.2825251es_ES
dc.identifier.publicationfirstpage501es_ES
dc.identifier.publicationissue2es_ES
dc.identifier.publicationlastpage508es_ES
dc.identifier.publicationvolume23es_ES
dc.relation.projectIDGobierno de España. TEC2014-53176-Res_ES
dc.type.versioninfo:eu-repo/semantics/submittedVersiones_ES
dc.rights.accessRightsopenAccesses_ES
dc.authorUAMEscudero Viñolo, Marcos (261850)
dc.authorUAMBescos Cano, Jesús (260863)
dc.facultadUAMEscuela Politécnica Superior


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