Computer-aided cirrhosis diagnosis via automatic liver capsule extraction and combined geometry-texture features

摘要

This paper presents a computer-aided system for automatic diagnosis of cirrhosis based on ultrasound images. We first propose a dynamic programming algorithm to automatically extract the liver capsule, and then the continuity and smoothness of capsule serve as an important guideline for image classification. Via the decomposition of the ultrasound image in spatial and gray scales, the density and entropy of suspected nodular areas are used to describe texture features of liver parenchyma. Finally, a trained SVM classifier is applied to classify the samples into normal, mild, moderate and severe clinical stages of the disease. Experiment results show that the proposed method achieves better performance than existing approaches. Moreover, it can be used as an efficient method for early cirrhosis diagnosis in consideration of its high accuracy in distinguishing between normal and abnormal cases.