Clustering Based Automatic Segmentation of Liver

Abstract

Identifying the liver region, calculation of the liver volume and determination of the vessel structure from abdominal computed tomography datasets are some of the essential steps in visualization prior to the hepatic surgery. Because of the high number of slices, manual segmentation of the liver is time consuming, tedious and depends on the experience. On the other hand, the automatic segmentation of the liver is very difficult task because of the gray level similarities of adjacent organs, injection of contrast media and partial volume effect problems. In this paper, we propose an algorithm that can handle these problems. Developed algorithm involves preprocessing, classification and post-processing stages. The proposed method is applied to 17 donor datasets and its performance is evaluated by area error rate calculations.