Çığır, Celal2016-01-082016-01-082011http://hdl.handle.net/11693/15752Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 69-79.Over 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.xvi, 79 leaves, illustrationsEnglishinfo:eu-repo/semantics/openAccessSalient point patternsTextureHistopathological image analysisAutomated cancer diagnosis and gradingColon cancerWB141 .C54 2011Diagnostic imaging--Digital techniques.Imaging systems in medicine.Image processing--Digital techniques.Computer simulation.Digital computer vision.Medical image analysis.Cancer--Diagnosis--Data processing.ThesisB130082