Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging A Review

• Published in: Ultrasonic imaging Vol. 39; no. 1; pp. 33 - 61
• Main Authors: Bharti, Puja, Mittal, Deepti, Ananthasivan, Rupa
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2017
• Subjects: Algorithms; Diagnosis; Diseases; Imaging; Liver; Medical services; Summaries; Ultrasound; ultrasound imaging; feature extraction; computer-aided characterization and diagnosis; diffuse liver diseases; classification; artificial intelligence; medical image processing
• ISSN: 0161-7346; 1096-0910
• DOI: 10.1177/0161734616639875

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable “second opinion” for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.