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Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

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Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

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dc.contributor.author Pereira, Joana es_ES
dc.contributor.author Colomer, Adrián es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.date.accessioned 2019-07-24T09:17:49Z
dc.date.available 2019-07-24T09:17:49Z
dc.date.issued 2018-11-09
dc.identifier.isbn 978-3-030-03492-4
dc.identifier.uri http://hdl.handle.net/10251/124074
dc.description.abstract Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out. es_ES
dc.description.sponsorship This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. es_ES
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Intelligent Data Engineering and Automated Learning – IDEAL 2018 es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;11314
dc.rights Reserva de todos los derechos es_ES
dc.subject Exudates es_ES
dc.subject Superpixels es_ES
dc.subject LBP es_ES
dc.subject Granulometries es_ES
dc.subject SVM es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-030-03493-1_19
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/732613/EU/Glaucoma – Advanced, LAbel-free High resolution Automated OCT Diagnostics/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BES-2014-067889/ES/BES-2014-067889/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Pereira, J.; Colomer, A.; Naranjo Ornedo, V. (2018). Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 174-183. https://doi.org/10.1007/978-3-030-03493-1_19 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) es_ES
dc.relation.conferencedate Noviembre 21-23,2018 es_ES
dc.relation.conferenceplace Madrid, Spain es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-030-03493-1_19 es_ES
dc.description.upvformatpinicio 174 es_ES
dc.description.upvformatpfin 183 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\372963 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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