dc.contributor.advisor | Demir, Çiğdem Gündüz | |
dc.contributor.author | Yücel, Simge | |
dc.date.accessioned | 2018-10-04T06:28:07Z | |
dc.date.available | 2018-10-04T06:28:07Z | |
dc.date.copyright | 2018-09 | |
dc.date.issued | 2018-09 | |
dc.date.submitted | 2018-09-02 | |
dc.identifier.uri | http://hdl.handle.net/11693/48075 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018. | en_US |
dc.description | Includes bibliographical references (leaves 49-51). | en_US |
dc.description.abstract | Colon 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.statementofresponsibility | by Simge Yücel. | en_US |
dc.format.extent | xi, 51 leaves : illustrations (some color) ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Histopathological Image Analysis | en_US |
dc.subject | Gland Segmentation | en_US |
dc.subject | Voronoi Diagram | en_US |
dc.subject | Structural Method | en_US |
dc.title | Rule based segmentation of colon glands | en_US |
dc.title.alternative | Kalın bağırsak bezlerinin kurala dayanarak bölütlenmesi | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B158597 | |