title: Texture analysis and classification of ultrasound liver images.
abstract: Ultrasound as a noninvasive imaging technique is widely used to diagnose liver diseases. Texture analysis and classification of ultrasound liver images have become an important research topic across the world. In this study, GLGCM (Gray Level Gradient Co-Occurrence Matrix) was implemented for texture analysis of ultrasound liver images first, followed by the use of GLCM (Gray Level Co-occurrence Matrix) at the second stage. Twenty two features were obtained using the two methods, and seven most powerful features were selected for classification using BP (Back Propagation) neural network. Fibrosis was divided into five stages (S0-S4) in this study. The classification accuracies of S0-S4 were 100%, 90%, 70%, 90% and 100%, respectively.
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Gao
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Peng
authors.forename: Huizhi
authors.initials: H
authors.lastname: Guo
authors.forename: Weifeng
authors.initials: W
authors.lastname: Liu
authors.forename: Tianxin
authors.initials: T
authors.lastname: Gao
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Xu
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Tang
mesh_terms: D000328:Adult; D001808:Blood Vessels / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology; D008103:Liver Cirrhosis / Q000473:pathology; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013499:Surface Properties; D014463:Ultrasonography
keywords: artificial neural network; liver fibrosis; texture features analysis; texture features extraction; ultrasonic image