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dc.contributor.authorTuba, Eva
dc.contributor.authorTuba, Milan
dc.contributor.authorSimian, Dana
dc.contributor.editorSkala, Václav
dc.date.accessioned2018-05-21T08:48:54Z-
dc.date.available2018-05-21T08:48:54Z-
dc.date.issued2017
dc.identifier.citationWSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 159-166.en
dc.identifier.isbn978-80-86943-45-9
dc.identifier.issn2464-4617
dc.identifier.uriwscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
dc.identifier.urihttp://hdl.handle.net/11025/29747
dc.description.abstractDigital images and digital image processing facilitated significant progress in numerous areas where medicine is an important one of them. Computer-aided detection and diagnostics systems are used to assist specialists in interpretation of medical digital images. One of the important research issues is detection and classification of the chronic obstructive pulmonary disease in lung CT images. In this paper we proposed a method for emphysema classification based on texture and intensity features. Only six different characteristics of the uniform local binary pattern and intensity histogram were used as input vector for support vector machine that was used as classifier. Feature vector was significantly reduced compared to the other state-of-the-art methods while the classification accuracy was increased. On images from standard dataset global accuracy of our proposed algorithm was 98.18% compared to 95.24% and 93.9% of two other compared algorithms.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG '2017: short communications proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectpodpora vektorových strojůcs
dc.subjectklasifikace plicních tkánícs
dc.subjectCT obrazycs
dc.subjectzpracování obrazucs
dc.subjectalgoritmus světluškacs
dc.subjectinteligence rojůcs
dc.titleSupport vector machine optimized by firefly algorithm for emphysema classification in lung tissue CT imagesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedsupport vector machinesen
dc.subject.translatedlung tissue classificationen
dc.subject.translatedCT imagesen
dc.subject.translatedimage processingen
dc.subject.translatedfirefly algorithmen
dc.subject.translatedswarm intelligenceen
dc.type.statusPeer-revieweden
Appears in Collections:WSCG '2017: Short Papers Proceedings

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