Liver segmentation in 3D CT data

Abstract

Segmentation of liver from 3D abdominal CT data is the basis of analysis of liver which is required to aid diagnosis and treatment of liver cancer. However, common clinical practice for liver segmentation relies on manual segmentation of CT images by the help of radiologist. Generally, this procedure is tedious and time-consuming. Therefore, fast and robust and accurate methods must be devised to automate liver segmentation process. There is a vast literature for automatic, semi-automatic and interactive liver segmentation methods based on various computer vision algorithms. Each of these methods possess some limitations due to highly varying structure of liver. In this thesis, we propose a semi-automatic liver segmentation algorithm based on an e ective combination of intensity distribution modeling, probabilistic atlases (PA) and graph cuts. Major contribution of this work is twofold. First of all, a novel PA construction methodology is proposed based on convex hulls of rough initial segmentation and reference manual delineations. Secondly, a new strategy to improve implicit gray level appearance models is proposed. In addition to that, we explain how to embed PA, gray level appearance models into graph cuts. The e ectiveness of proposed algorithm was demonstrated in clinical CT images. Evaluation scores show that proposed method provides results comparable with manual segmentation of a human who has adequate training in liver segmentation.