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DC poleHodnotaJazyk
dc.contributor.authorVaitulevicius, Aleksas
dc.contributor.authorTreigys, Povilas
dc.contributor.authorBernataviciene, Jolita
dc.contributor.authorSurkant, Roman
dc.contributor.authorMarkeviciute, Jurgita
dc.contributor.authorNaruseviciute, Ieva
dc.contributor.authorTrakymas, Mantas
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T08:30:41Z
dc.date.available2022-09-01T08:30:41Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 48-55.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49578
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectrakovina prostatycs
dc.subjectfunkční analýza datcs
dc.subjectdynamická MRI se zvýšeným kontrastemcs
dc.subjectstrojové učenícs
dc.subjectkomponentcs
dc.titleDCE MRI Modality Investigation for Cancerous Prostate Region Detection: Case Analysisen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedTypically, prostate evaluation is done by using different imaging sequences of magnetic resonance imaging. Dy- namic contrast enhancement, one of such scanning modalities, allow to spot higher vascular permeability and density caused by the malignant tissue. Authors of this paper investigate the ability to identify malignant prostate regions by the functional data analysis and standard machine learning techniques. The dynamic contrast enhanced images of the prostate are divided into the regions and based on those time-signal intensity curves are calculated. Two classification approaches: functional k-Nearest Neighbors and machine learning Support Vector Machine are used to model signal curve behavior on temporal variation matrix and timestamp based prostate region division of image data. Preliminary research shows that both functional data analysis and machine learning classification methods are able to identify highest saturation timestamp that gives best tissue classification results on timestamp based dynamic contrast enhanced region map obtained by Simple Linear Iterative Clustering algorithm. Cancer region classification results are better when the dynamic contrast enhanced images are subdivided into regions at each timestamp than when using a temporal variation matrix.en
dc.subject.translatedprostate canceren
dc.subject.translatedfunctional data analysisen
dc.subject.translatedmachine learningen
dc.subject.translatedcomponenten
dc.subject.translateddynamic contrast-enhanced MRIen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.7
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2022: Full Papers Proceedings

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