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dc.contributor.authorZortea, Maciel
dc.contributor.authorSkrøvseth, Stein Olav
dc.contributor.authorSchopf, Thomas Roger Griesbeck
dc.contributor.authorKirchesch, Herbert M.
dc.contributor.authorGodtliebsen, Fred
dc.date.accessioned2012-03-14T15:29:40Z
dc.date.available2012-03-14T15:29:40Z
dc.date.issued2011
dc.description.abstractAccurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.en
dc.identifier.citationInternational Journal of Biomedical Imaging Volume 2011, Article ID 972648, 19 pagesen
dc.identifier.cristinIDFRIDAID 831892
dc.identifier.doidoi: 10.1155/2011/972648
dc.identifier.issn1687-4188
dc.identifier.urihttps://hdl.handle.net/10037/3975
dc.identifier.urnURN:NBN:no-uit_munin_3697
dc.language.isoengen
dc.publisherHindawi Publishing Corporationen
dc.rights.accessRightsopenAccess
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en
dc.titleAutomatic Segmentation of Dermoscopic Images by Iterative Classificationen
dc.typeJournal articleen
dc.typeTidsskriftartikkelen
dc.typePeer revieweden


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