title: A Computer-Aided Diagnosis Scheme For Detection Of Fatty Liver In Vivo Based On Ultrasound Kurtosis Imaging.
abstract: Fatty liver disease is a common disease caused by alcoholism, obesity, and diabetes, resulting in triglyceride accumulation in hepatocytes. Kurtosis coefficient, a measure of the peakedness of the probability distribution, has been applied to the analysis of backscattered statistics for characterizing fatty liver. This study proposed ultrasound kurtosis imaging as a computer-aided diagnosis (CAD) method to visually and quantitatively stage the fatty liver. A total of 107 patients were recruited to participate in the experiments. The livers were scanned using a clinical ultrasound scanner with a 3.5-MHz curved transducer to acquire the raw ultrasound backscattered signals for kurtosis imaging. The kurtosis image was constructed using the sliding window technique. Experimental results showed that kurtosis imaging has the ability to visualize and quantify the variation of backscattered statistics caused by fatty infiltration. The kurtosis coefficient corresponding to liver parenchyma decreased from 5.41 ± 0.89 to 3.68 ± 0.12 with increasing the score of fatty liver from 0 (normal) to 3 (severe), indicating that fatty liver reduces the degree of peakedness of backscattered statistics. The best performance of kurtosis imaging was found when discriminating between normal and fatty livers with scores ≥1: the area under the curve (AUC) is 0.92 at a cutoff value of 4.36 (diagnostic accuracy =86.9 %, sensitivity =86.7 %, specificity =87.0 %). The current findings suggest that kurtosis imaging may be useful in designing CAD tools to assist in physicians in early detection of fatty liver.
authors.affiliation: Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.|Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
authors.forename: Hsiang-Yang
authors.initials: HY
authors.lastname: Ma
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
authors.forename: Zhuhuang
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
authors.forename: Shuicai
authors.initials: S
authors.lastname: Wu
authors.affiliation: Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan. ylw0518@cgmh.org.tw.|Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan. ylw0518@cgmh.org.tw.
authors.forename: Yung-Liang
authors.initials: YL
authors.lastname: Wan
authors.affiliation: Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan. tsuiph@mail.cgu.edu.tw.|Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan. tsuiph@mail.cgu.edu.tw.
authors.forename: Po-Hsiang
authors.initials: PH
authors.lastname: Tsui
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005234:Fatty Liver / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D014463:Ultrasonography
keywords: Backscattered statistics; Computer-aided diagnosis; Fatty liver; Kurtosis imaging; Ultrasound tissue characterization