Object-oriented segmentation of cell nuclei in fluorescence microscopy images

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buir.contributor.authorKoyuncu, Can Fahrettin
buir.contributor.authorGunduz Demir, Cigdem
dc.citation.epage1028en_US
dc.citation.issueNumber10en_US
dc.citation.spage1019en_US
dc.citation.volumeNumber93en_US
dc.contributor.authorKoyuncu, Can Fahrettinen_US
dc.contributor.authorCetin Atalay, R.en_US
dc.contributor.authorGunduz Demir, Cigdemen_US
dc.date.accessioned2019-02-21T16:01:40Z
dc.date.available2019-02-21T16:01:40Z
dc.date.issued2018en_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.abstractCell nucleus segmentation remains an open and challenging problem especially to segment nuclei in cell clumps. Splitting a cell clump would be straightforward if the gradients of boundary pixels in-between the nuclei were always higher than the others. However, imperfections may exist: inhomogeneities of pixel intensities in a nucleus may cause to define spurious boundaries whereas insufficient pixel intensity differences at the border of overlapping nuclei may cause to miss some true boundary pixels. In contrast, these imperfections are typically observed at the pixel-level, causing local changes in pixel values without changing the semantics on a large scale. In response to these issues, this article introduces a new nucleus segmentation method that relies on using gradient information not at the pixel level but at the object level. To this end, it proposes to decompose an image into smaller homogeneous subregions, define edge-objects at four different orientations to encode the gradient information at the object level, and devise a merging algorithm, in which the edge-objects vote for subregion pairs along their orientations and the pairs are iteratively merged if they get sufficient votes from multiple orientations. Our experiments on fluorescence microscopy images reveal that this high-level representation and the design of a merging algorithm using edge-objects (gradients at the object level) improve the segmentation results.
dc.description.sponsorshipThis work was supported by the Turkish Academy of Sciences under the Distin guished Young Scientist Award Program (TUBA GEBIP).
dc.embargo.release2019-10-22en_US
dc.identifier.doi10.1002/cyto.a.23594
dc.identifier.eissn1552-4930en_US
dc.identifier.issn1552-4922
dc.identifier.urihttp://hdl.handle.net/11693/49895
dc.language.isoEnglish
dc.publisherWiley-Liss
dc.relation.isversionofhttps://doi.org/10.1002/cyto.a.23594
dc.relation.projectTürkiye Bilimler Akademisi, TÜBA
dc.source.titleCytometry Part Aen_US
dc.subjectFluorescence microscopy imagingen_US
dc.subjectNucleus detectionen_US
dc.subjectNucleus segmentationen_US
dc.subjectObject-based representationen_US
dc.titleObject-oriented segmentation of cell nuclei in fluorescence microscopy imagesen_US
dc.typeArticleen_US
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