Classification of Liver Diseases Based on Ultrasound Image Texture Features

Abstract

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.

Keywords

• classification
• F-score
• gray-level co-occurrence matrix (GLCM)
• gray-level run-length matrix (GLRLM)
• hepatocellular carcinoma (HCC)
• liver cancer
• liver abscess
• image texture
• sequential backward selection (SBS)
• sequential forward selection (SFS)
• support vector machine (SVM)
• ultrasound images