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dc.contributor.advisorDemir, Çiğdem Gündüz
dc.contributor.authorKandemir, Melih
dc.date.accessioned2016-01-08T18:17:38Z
dc.date.available2016-01-08T18:17:38Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/11693/15373
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2008.en_US
dc.descriptionIncludes bibliographical references leaves 72-79.en_US
dc.description.abstractHistopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in this visual examination, many computational methods have been proposed. These methods represent a tissue with a set of mathematical features and use these features in further analysis of the biopsy. For the tissue types that contain glandular structures, one of these analyses is to examine the changes in these glandular structures. For such analyses, the very first step is to segment the tissue into its glands. In this thesis, we present an object-based method for the segmentation of colon glands. In this method, we propose to decompose the image into a set of primitive objects and use the spatial distribution of these objects to determine the locations of glands. In the proposed method, pixels are first clustered into different histological structures with respect to their color intensities. Then, the clustered image is decomposed into a set of circular primitive objects (white objects for luminal regions and black objects for nuclear regions) and a graph is constructed on these primitive objects to quantify their spatial distribution. Next, the features are extracted from this graph and these features are used to determine the seed points of gland candidates. Starting from these seed points, the inner glandular regions are grown considering the locations of black objects. Finally, false glands are eliminated based on another set of features extracted from the identified inner regions and exact boundaries of the remaining true glands are determined considering the black objects that are located near the inner glandular regions. Our experiments on the images of colon biopsies have demonstrated that our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland segmentation algorithms. Our experiments have also shown that the object-based structure of the method provides tolerance to artifacts resulting from variances in biopsy staining and sectioning procedures. This proposed method offers an infrastructure for further analysis of glands for the purpose of automated cancer diagnosis and grading.en_US
dc.description.statementofresponsibilityKandemir, Melihen_US
dc.format.extentxiii, 79 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHistopathological image analysisen_US
dc.subjectGland segmentationen_US
dc.subjectObject-based segmentationen_US
dc.subjectObject-graphsen_US
dc.subject.lccWI529 .K35 2008en_US
dc.subject.lcshColon (Anatomy)--Cancer--Histopathology.en_US
dc.subject.lcshObject-oriented programming (Computer science)en_US
dc.subject.lcshComputer graphics.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshComputer vision.en_US
dc.titleSegmentation of colon glands by object graphsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidBILKUTUPB109223


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