title: [Computer-aided diagnosis of fatty liver based on ultrasonic images].
abstract: This study aims to provide a computer-aided method for the diagnosis of fatty liver by B-scan ultrasonic imaging. Fatty liver is referred to the infiltration of triglycerides and other fats of the liver cells, which affected the texture of liver tissue. In this paper, some features including mean intensity ratio, as well as angular second moment, entropy and inverse differential moment of gray level co-occurrence matrix were extracted from B-scan ultrasonic liver images. Feature vectors which indicated two classes of images were created with the four features. Then we used kappa-means clustering algorithm, self-organized feature mapping (SOFM) artificial neural network and back-propagation (BP) artificial neural network to classify these vectors. The accuracy rate of kappa-means clustering algorithm was 100% for normal liver and 63.6% for fatty liver. The results of SOFM neural network showed that the accuracy rate was 84.8% for normal liver and 90.9% for fatty liver. The accuracy rate of neural network was 100% both for normal liver and fatty liver. This technology could detect the characteristics of B-scan images of normal liver and fatty liver more accurately. It could greatly improve the accuracy of the diagnosis of fatty liver.
authors.affiliation: Biomedical Engineering Center, Sichuan University, Chengdu 610065, China.
authors.forename: Xiaoyi
authors.initials: X
authors.lastname: Wang
authors.forename: Jiangli
authors.initials: J
authors.lastname: Lin
authors.forename: Deyu
authors.initials: D
authors.lastname: Li
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
authors.forename: Changqiong
authors.initials: C
authors.lastname: Zheng
authors.forename: Yinrong
authors.initials: Y
authors.lastname: Cheng
mesh_terms: D003937:Diagnosis, Differential; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D014463:Ultrasonography