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dc.contributor.advisorDemir, Çiğdem Gündüz
dc.contributor.authorKoyuncu, Can Fahrettin
dc.date.accessioned2018-09-13T07:58:26Z
dc.date.available2018-09-13T07:58:26Z
dc.date.copyright2018-09
dc.date.issued2018-09
dc.date.submitted2018-09-12
dc.identifier.urihttp://hdl.handle.net/11693/47862
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 100-110).en_US
dc.description.abstractHigh-throughput microscopy systems have become popular recently, which facilitate to acquire boundless microscopy images without requiring human intervention. However, the analysis of such amount of images using conventional methods is nearly impractical since the analysis can take up to months. Additionally, a considerable amount of observer variability may occur since the analysis completely relies on interpretation of the analysts. As a remedy for that, automated decision support systems, which are objective and rapid, have gained more attention. Since these systems conduct analyses at cellular level, they require a cell segmentation model, results of which directly affect the performance of the entire system. There are several challenges in cell segmentation, each of which should be addressed carefully in order to have an accurate cell segmentation model. One challenge is that cells can be grown in multilayer on the plate, which makes them appear as clusters on the image. Segmentation of these cells requires extra effort since they should be splitted from each other. Another challenge is the imperfections on the image such as inhomogeneities of pixel intensities in a cell and insu cient pixel intensity differences at the border of overlapping cells. Yetanother challenge is the heterogeneity in the morphological characteristics of cells. Depending on cell line types, cells may appear in various outlooks. Developing a generic cell segmentation model, which can handle different cells' outlooks and imperfections, is an open and challenging problem. In order to tackle with these challenges, we deal with the cell segmentation problem in two parts: (1)We focus on finding a new representation for microscopy images, helping us simplify the cell segmentation problem, so that imperfections in cells and inhomogeneities in their visual properties can be alleviated, and cell locations can be emphasized better. (2) We focus on developing a more advanced cell segmentation method, with the motivation that it is almost impossible to obtain a perfect representation in practice. Thus, we work on developing more sophisticated cell segmentation techniques that overcome deficiencies on the representation. To this end, this thesis introduces three new cell segmentation models, two of which introduce a new cell representation technique as well. In our experiments, we tested our algorithms on various microscopy images obtained under the uorescence and phase contrast microscopies and compared them with the previous cell segmentation methods. Our experiments show that the proposed algorithms are more effective in segmenting cells and more robust to the aforementioned challenges.en_US
dc.description.statementofresponsibilityby Can Fahrettin Koyuncu.en_US
dc.format.extentxviii, 110 leaves : illustrations (some color), charts ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicroscopy Imagingen_US
dc.subjectFluorescence Microscopy Phase Contrast Microscopyen_US
dc.subjectCell Segmentationen_US
dc.subjectCell Detectionen_US
dc.subjectNucleus Segmentationen_US
dc.subjectNucleus Detectionen_US
dc.titleAutomated cell analysis in microscopy imagesen_US
dc.title.alternativeMikroskopi görüntülerinde otomatik hücre analizien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US
dc.identifier.itemidB158948
dc.embargo.release2019-01-01


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