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dc.contributor.advisorDemir, Çiğdem Gündüz
dc.contributor.authorÇığır, Celal
dc.date.accessioned2016-01-08T18:24:07Z
dc.date.available2016-01-08T18:24:07Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15752
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 69-79.en_US
dc.description.abstractOver the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.en_US
dc.description.statementofresponsibilityÇığır, Celalen_US
dc.format.extentxvi, 79 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSalient point patternsen_US
dc.subjectTextureen_US
dc.subjectHistopathological image analysisen_US
dc.subjectAutomated cancer diagnosis and gradingen_US
dc.subjectColon canceren_US
dc.subject.lccWB141 .C54 2011en_US
dc.subject.lcshDiagnostic imaging--Digital techniques.en_US
dc.subject.lcshImaging systems in medicine.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshComputer simulation.en_US
dc.subject.lcshDigital computer vision.en_US
dc.subject.lcshMedical image analysis.en_US
dc.subject.lcshCancer--Diagnosis--Data processing.en_US
dc.titleHistopathological image classification using salient point patternsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB130082


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