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AUTOMATIC COLON SEGMENTATION USING CELLULAR NEURAL NETWORK FOR THE DETECTION OF COLORECTAL POLYPS
(Istanbul Univ, Fac Engineering, 2007) Kilic, Niyazi; Osman, Onur; Ucan, Osman N.; Demirel, Kemal
In this paper, an automatic colon segmentation method for Computed Tomography (CT) colonography is presented. Colon segmentation is considered in order to prevent the time consumption while searching polyps out of the colon region and reduce radiologists' interpretation time. The proposed method is the combination of pre-processing and Cellular Neural Networks (CNN). Also recurrent perceptron learning algorithm (RPLA) is used for CNN training. Original CT images are passed through a threshold and then CNN is used to erase unrelated small objects and smooth sharp corners. It is expected automatic colon segmentation will improve the radiologists' diagnostic performance.
COMPUTER NETWORK OPTIMIZATION USING GENETIC ALGORITHM
(Istanbul Univ, Fac Engineering, 2006) Akbulut, Olcay; Osman, Onur; Ucan, Osman N.
In this paper, Genetic Algorithm (GA) is proposed as optimization software to find the shortest path of various computer networks. It deals with different method concerning the placement routers, routes the packages. The genetic based algorithm defines an optimum way when a computer network system is constructed. Genetic Algorithm gives better results regarding other classical methods as the number of nodes of the network increases.