A note of liver cirrhosis classification on M-mode ultrasound images by higher-order local auto-correlation features

Abstract

Ultrasound images are widely used for diagnosis of liver cirrhosis. In liver cirrhosis classification using M-mode ultrasound images, Zhou's method has been shown to be effective. However, in Zhou's approach, the liver cirrhosis classification performance depends on the accuracy of the abdominal aorta wall extraction. Therefore, we examine to classify the liver cirrhosis not using the abdominal aorta wall extraction process. In this paper, we propose a liver cirrhosis classification method using higher-order local auto-correlation (HLAC) features. Furthermore, we also propose to use image processing techniques of a thresholding technique and a shading technique to effectively extract the HLAC features. Experimental results show that the proposed method is promising.