Akademik Veri Yönetim Sistemi
Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) / Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), Tampere, Finlandiya, 01 Haziran 2017, cilt.65, ss.414-417
Computed tomography (CT) imaging is widely used for control and diagnosis of diseases nowadays. Segmentation of medical images is quite important, especially for diagnosis arid treatment of cancer. In this study, similar and different tissues in CT images of liver are determined by using two different methods; watershed rand histogram thresholding. The images have been preprocessed before segmentation. First, images are converted to grayscale. Next, they are smoothed with a bilateral filter. To apply the watershed technique, edges are extracted with a Gradient operator. The over segmentation of the watershed method is overcome by merging the closest segments in terms of their features. The merging is obtained via vector quantization of the features; fuzzy c-means clustering and k-means clustering algorithms by grouping mean and standard deviation of segments. The images are divided into five segments corresponding to liver, vertebra, tumor, lining and others. In the histogram thresholding method, multi thresholds are obtained with Otsu method from the smoothed image and segmentation has been performed. The results of two approaches have been compared. Pixel value, directional derivatives (DD), local binary patterns (LBP), difference of pixel with its neighborhood (DP) are employed as features to determine the segment class. Classifications of the regions were obtained from a single pixel and segment separately by dividing 44 liver images to two training (22 images) and test sets (22 images). The best accuracy for classification from a pixel was obtained 95.64 % with difference of pixel with its neighborhood feature whereas 98.88 % was obtained for categorization from the whole segment with directional derivatives feature by using histogram thresholding algorithm. This application may help physicians to distinguish and determine the similar or different tissues in the medical images.