Iterative H-minima-based marker-controlled watershed for cell nucleus segmentation

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buir.contributor.authorKoyuncu, Can Fahrettin
buir.contributor.authorAkhan, Ece
buir.contributor.authorGunduz Demir, Çiğdem
dc.citation.epage349en_US
dc.citation.issueNumber4en_US
dc.citation.spage338en_US
dc.citation.volumeNumber89en_US
dc.contributor.authorKoyuncu, Can Fahrettinen_US
dc.contributor.authorAkhan, Eceen_US
dc.contributor.authorErsahin, T.en_US
dc.contributor.authorCetin Atalay, R.en_US
dc.contributor.authorGunduz Demir, Çiğdemen_US
dc.date.accessioned2018-04-12T10:58:30Z
dc.date.available2018-04-12T10:58:30Z
dc.date.issued2016en_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentInterdisciplinary Program in Neuroscience (NEUROSCIENCE)en_US
dc.departmentAysel Sabuncu Brain Research Center (BAM)en_US
dc.description.abstractAutomated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts.en_US
dc.identifier.doi10.1002/cyto.a.22824en_US
dc.identifier.issn1552-4922
dc.identifier.urihttp://hdl.handle.net/11693/36961
dc.language.isoEnglishen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1002/cyto.a.22824en_US
dc.source.titleCytometry. Part Aen_US
dc.subjectAutomateden_US
dc.subjectFluorescence microscopy imagingen_US
dc.subjectH-minima transformen_US
dc.subjectNucleus segmentationen_US
dc.subjectWatersheden_US
dc.subjectBiological markeren_US
dc.subjectAlgorithmen_US
dc.subjectAutomated pattern recognitionen_US
dc.subjectCell lineen_US
dc.subjectCell nucleusen_US
dc.subjectHumanen_US
dc.subjectImage enhancementen_US
dc.subjectImage processingen_US
dc.subjectProceduresen_US
dc.subjectAlgorithmsen_US
dc.subjectBiomarkersen_US
dc.subjectCell lineen_US
dc.subjectCell nucleusen_US
dc.subjectImage enhancementen_US
dc.subjectComputer-assisteden_US
dc.subjectPattern recognitionen_US
dc.titleIterative H-minima-based marker-controlled watershed for cell nucleus segmentationen_US
dc.typeArticleen_US

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Department of Molecular Biology and Genetics
Aysel Sabuncu Brain Research Center (BAM)
Department of Computer Engineering