Automatic segmentation of computed tomography images of liver using watershed and thresholding algorithms

Computed tomography (CT) imaging is widely used for control and diagnosis of diseases today. Segmentation of medical images is quite important especially for diagnosis and treatment of cancer. In this study, segments in CT images of liver are determined by using two different methods; watershed algorithm and histogram thresholding method. 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, standard deviation and features of segments which are also used in classification. The images are divided into five segments corresponding to liver, vertebra, tumour, lining and others. In case of histogram thresholding, 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, local binary patterns, difference of pixel with its neighborhood haven been employed as features to determine the segment class. Classifications of the regions have been obtained from a single pixel with linear discriminant analysis classifier and segment with k- nearest neighbor classifier by dividing 25 liver images to two training (13 images) and test sets (12 images). The best accuracy was obtained % 95.57 for classification from a pixel with difference of pixel with its neighborhood feature whereas % 100 is obtained for categorization from the segment with directional derivatives and difference of pixel with its neighborhood features by using histogram thresholding algorithm. This application may help physicians in terms of providing insight with the tissues before the surgery by segmenting tissues in the medical images.