Kalın bağırsak bezlerinin kurala dayanarak bölütlenmesi

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 quantification 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 defining a set of rules on this new representation, and to devise an iterative algorithm that selects a subset of these candidates based on their fitness 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 defining the rules and the fitness 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.