Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images

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dc.citation.epage1131en_US
dc.citation.issueNumber6en_US
dc.citation.spage1121en_US
dc.citation.volumeNumber32en_US
dc.contributor.authorArslan, S.en_US
dc.contributor.authorErsahin, T.en_US
dc.contributor.authorCetin-Atalay, R.en_US
dc.contributor.authorGunduz-Demir, C.en_US
dc.date.accessioned2016-02-08T09:38:14Z
dc.date.available2016-02-08T09:38:14Z
dc.date.issued2013en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractMore rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms. © 2012 IEEE.en_US
dc.identifier.doi10.1109/TMI.2013.2255309en_US
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/20933
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TMI.2013.2255309en_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectAttributed relational graphen_US
dc.subjectGraphen_US
dc.subjectModel-based segmentationen_US
dc.subjectNucleus segmentationen_US
dc.subjectAttributed relational graphen_US
dc.subjectFluorescence microscopy imagingen_US
dc.subjectCytologyen_US
dc.subjectMonolayersen_US
dc.subjectImage segmentationen_US
dc.subjectAccuracyen_US
dc.subjectAlgorithmen_US
dc.subjectCell maturationen_US
dc.subjectCell nucleusen_US
dc.subjectCell nucleus segmentationen_US
dc.subjectCellular distributionen_US
dc.subjectComparative studyen_US
dc.subjectFluorescence microscopyen_US
dc.subjectHumanen_US
dc.subjectHuman cellen_US
dc.subjectLiver cell carcinomaen_US
dc.subjectAlgorithmsen_US
dc.titleAttributed relational graphs for cell nucleus segmentation in fluorescence microscopy imagesen_US
dc.typeArticleen_US

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