Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm

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dc.contributor.authorGálvez, Akemi
dc.contributor.authorFister, Iztok
dc.contributor.authorFister, Iztok
dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorIglesias, Andrés
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:52:18Z
dc.date.available2024-07-24T11:52:18Z
dc.date.issued2018
dc.descriptionPublisher Copyright: © 2018, Springer Nature Switzerland AG.
dc.description.abstractThis paper addresses the problem of automatic fitting of feature points for border detection of skin lesions. This problem is an important task in segmentation of dermoscopy images for semi-automatic early diagnosis of melanoma and other skin lesions. Given a set of feature points selected by a dermatologist, we apply a powerful nature-inspired metaheuristic optimization method called bat algorithm to obtain the free-form parametric Bézier curve that fits the points better in the least-squares sense. Our experimental results on two examples of skin lesions show that the method performs quite well and might be applied to automatic fitting of feature points for border detection in medical images.en
dc.description.sponsorshipAcknowledgments. This research work has been kindly supported by the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program), grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Regional Development Funds (AEI/FEDER-UE), the project #JU12 of SODERCAN and European Regional Development Funds (SODERCAN/FEDER-UE), the Slovenian Research Agency (Research Core Funding No. P2-0057), and the project EMAITEK of the Basque Government. This research work has been kindly supported by the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program), grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Regional Development Funds (AEI/FEDER-UE), the project #JU12 of SODERCAN and European Regional Development Funds (SODERCAN/FEDER-UE), the Slovenian Research Agency (Research Core Funding No. P2-0057), and the project EMAITEK of the Basque Government.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationGálvez , A , Fister , I , Fister , I , Osaba , E , Del Ser , J & Iglesias , A 2018 , Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm . in Studies in Computational Intelligence . Studies in Computational Intelligence , vol. 798 , Springer Verlag , pp. 357-368 . https://doi.org/10.1007/978-3-319-99626-4_31
dc.identifier.doi10.1007/978-3-319-99626-4_31
dc.identifier.issn1860-949X
dc.identifier.urihttps://hdl.handle.net/11556/2176
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85053473678&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofStudies in Computational Intelligence
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.relation.projectIDFEDER-UE
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 778035
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Economía y Competitividad, MINECO, 2017-89275-R
dc.relation.projectIDJavna Agencija za Raziskovalno Dejavnost RS, ARRS, P2-0057
dc.relation.projectIDHorizon 2020
dc.relation.projectIDEuropean Regional Development Fund, ERDF
dc.relation.projectIDAgencia Estatal de Investigación, AEI
dc.relation.projectIDSociedad para el Desarrollo de Cantabria, SODERCAN
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsBat algorithm
dc.subject.keywordsBorder detection
dc.subject.keywordsComputational intelligence
dc.subject.keywordsMedical images
dc.subject.keywordsNature-inspired metaheuristic techniques
dc.subject.keywordsSkin lesion
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleAutomatic fitting of feature points for border detection of skin lesions in medical images with bat algorithmen
dc.typebook part
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