B-scan Images Analyzed By CNN And Cooccerrence Matriz

摘要：

In this paper, we combine cellular neural network (CNN) and gray step co-occurrence matrix to process B-scan images of fatty patients livers. We deal with the B-scan images of fatty patients livers by the edge detection cellular neural network, and then analyze the B-scan image features, including the co-occurrence matrixs contrast (Contrast), correlation (Correlation), energy (Energy) and homogeneity (Homogeneity). The value of Contrast on 0° direction seems to correlate to the degree of the damage of patients livers. It is expected that the method provided in this paper will be helpful to the diagnosis of biomedical images.

关键词： B-scan image Fatty liver tezture analysis Cellular neural networks Co-occurrence matriz

作者: Guodong Li Huiming Song Wen Wang Jianghe Wang Huiwen Hong Yanling Liu

作者单位: School of Mathematics and Physics North China Electric Power University Beijing, 102206, China Traditional Medical Research Institute of China XiYuan Hospital Chinese Beijing 100871, PR China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 2456-2459

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）