Texture analysis and classification of ultrasound liver images
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
Note: [] The study is supported by National Natural Science Foundation of China (No. 81271568)
Note: [] Corresponding author: Yuanqing Xu and Xiaoying Tang. E-mail: bitxyq@bit.edu.cn or xiaoying@bit.edu.cn
Note: [] Corresponding author: Yuanqing Xu and Xiaoying Tang. E-mail: bitxyq@bit.edu.cn or xiaoying@bit.edu.cn
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, andseven 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.
Keywords: liver fibrosis, ultrasonic image, texture features analysis, texture features extraction, artificial neural network
