title: Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography.
abstract: Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.
authors.affiliation: Department of Computer Engineering, Silla University, Busan 617-736, Republic of Korea.
authors.forename: Kwang Baek
authors.initials: KB
authors.lastname: Kim
authors.affiliation: Department of Radiology, School of Medicine, Pusan National University, Pusan National University Hospital, Busan 602-739, Republic of Korea.
authors.forename: Chang Won
authors.initials: CW
authors.lastname: Kim
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*