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dc.contributor.authorVičar, Tomáš
dc.date.accessioned2019-03-04T10:06:00Z
dc.date.available2019-03-04T10:06:00Z
dc.date.issued2018cs
dc.identifier.citationProceedings of the 24th Conference STUDENT EEICT 2018. s. 590-594. ISBN 978-80-214-5614-3cs
dc.identifier.isbn978-80-214-5614-3
dc.identifier.urihttp://hdl.handle.net/11012/138302
dc.description.abstractThe life cell microscopic imaging is a standard approach for studying of cancer cell morphology and behaviour during some treatment. In the dense cell cultures, tracking each cell nucleus is challenging task due to cell overlap and interactions. Moreover, for time-lapse sequences (lasting typically 20-30 hours) the robust automatic cell tracking is needed. This paper describes new method for fluorescence nuclei tracking based on Gaussian mixture model (GMM), and additionally, GMM modification allowing application to the images is also introduced. Method is mainly designed for robustness - tracking the highest possible number of nuclei in the whole sequence. Proposed algorithm proved to by very reliable with 80% of correctly tracked nuclei.en
dc.formattextcs
dc.format.extent590-594cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 24th Conference STUDENT EEICT 2018en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectFluorescence nuclei imagesen
dc.subjectnuclei trackingen
dc.subjectGaussian mixture modelen
dc.titleRobust Cell Nuclei Tracking Using Gaussian Mixture Shape Modelen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date26.04.2018cs
but.event.titleStudent EEICT 2018cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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