Computer aided segmentation application of liver ct images

In recent years, computer assisted detection(CAD) systems have also found a large place in the medical field due to frequent use of imaging techniques. The most important process in CADsystems is the correct segmentation phase. In this thesis, computer assisted automated methods and software were developed to divide the liver over computerized tomography(CT) images. For segmentation operations, the area magnification (RG) and fuzzy c-average (FCM) algorithms were used. In order to determine the success of these algorithms, support has been obtained from a specialist physician in the field. For this purpose, computer-aided segmentation results were compared with RG and FCM algorithms using expert selection criteria. The comparison criteria were used. In the experimental studies performed on 88 BT image, the average performance values were 91.15% in RG algorithm and 75.16% in FCM algorithm according to jaccard similarity criteria. As a result, segmentation operations with the zone magnification algorithm were more successful. In addition, statistical significance tests of the quantitative values as a result of similarity measurements have been performed and more successful segmentation results obtained by the RG algorithm revealed a significant difference was found. In addition, comparisons were made with similar and semantic tests, and it was concluded that the splitting process with RG was faster. In this study, it was observed that the computer-aided method and the developed software for liver division could be used as a secondary tool in the decision-making process of physicians.