Smart markers for watershed-based cell segmentation

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dc.citation.epage48664-11en_US
dc.citation.issueNumber11en_US
dc.citation.spage48664-1en_US
dc.citation.volumeNumber7en_US
dc.contributor.authorKoyuncu, C. F.en_US
dc.contributor.authorArslan, S.en_US
dc.contributor.authorDurmaz, I.en_US
dc.contributor.authorCetin Atalay, R.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2015-07-28T12:04:20Z
dc.date.available2015-07-28T12:04:20Z
dc.date.issued2012-11-12en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractAutomated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of "smart markers'' for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.en_US
dc.description.provenanceMade available in DSpace on 2015-07-28T12:04:20Z (GMT). No. of bitstreams: 1 10.1371-journal.pone.0048664.pdf: 3383369 bytes, checksum: 210d075048a78f9cf6fe1851580f51da (MD5)en
dc.identifier.doi10.1371/journal.pone.0048664en_US
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11693/13009
dc.language.isoEnglishen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0048664en_US
dc.source.titlePublic Library of Science Oneen_US
dc.subjectTime-lapse Microscopyen_US
dc.subjectUnsupervised Segmentationen_US
dc.subjectAutomated Segmentationen_US
dc.subjectPhase Identificationen_US
dc.subjectClustered Nucleien_US
dc.subjectImagesen_US
dc.subjectClassificationen_US
dc.subjectTackingen_US
dc.subjectModelen_US
dc.titleSmart markers for watershed-based cell segmentationen_US
dc.typeArticleen_US

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