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dc.contributor.advisorDemir, Çiğdem Gündüz
dc.contributor.authorYücel, Simge
dc.date.accessioned2018-10-04T06:28:07Z
dc.date.available2018-10-04T06:28:07Z
dc.date.copyright2018-09
dc.date.issued2018-09
dc.date.submitted2018-09-02
dc.identifier.urihttp://hdl.handle.net/11693/48075
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 49-51).en_US
dc.description.abstractColon adenocarcinoma, which accounts for more than 90 percent of all colorectal cancers, originates from epithelial cells that form colon glands. Thus, for its diagnosis and grading, it is important to examine the distortions in the organizations of these epithelial cells, and hence, the deformations in the colon glands. Therefore, localization of the glands within a tissue and quanti cation of their deformations is essential to develop an automated or a semi-automated decision support system. With this motivation, this thesis proposes a new structural segmentation algorithm to detect glands in a histopathological tissue image. This structural algorithm proposes to transform the histopathological image into a new representation by locating a set of primitives using the Voronoi diagram, to generate gland candidates by de ning a set of rules on this new representation, and to devise an iterative algorithm that selects a subset of these candidates based on their tness scores. The main contribution of this thesis is the following: The representation introduced by this proposed algorithm enables us to better encode the colon glands by de ning the rules and the tness scores with respect to the appearance of the glands in a colon tissue. This representation and encoding have not been used by the previous studies. The experimental results of our algorithm show that this proposed algorithm improves the segmentation results of its pixel-based and structural counterparts without applying any further processing.en_US
dc.description.statementofresponsibilityby Simge Yücel.en_US
dc.format.extentxi, 51 leaves : illustrations (some color) ; 30 cm.en_US
dc.language.isoen_USen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHistopathological Image Analysisen_US
dc.subjectGland Segmentationen_US
dc.subjectVoronoi Diagramen_US
dc.subjectStructural Methoden_US
dc.titleRule based segmentation of colon glandsen_US
dc.title.alternativeKalın bağırsak bezlerinin kurala dayanarak bölütlenmesien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158597


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