Medical Image De-noising Eztended Model Based on Independent Component Analysis and Dynamic Fuzzy Function
Independent component analysis (ICA) is a statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables. This paper proposes a new extended model for CT medical image de-noising, which is using independent component analysis and dynamic fuzzy theory. Firstly, a random matrix was produce to separate the CT image into a separated image for estimate. Then dynamic fuzzy theory was applied to construct a series of adaptive membership functions to generate the weights degree of truth. At last, the weights degree was applied to optimize the value of matrix for image reconstruction. By applying this model, the selection of matrix could be optimized scientifically and self-adaptively. By contrast, this approach could remove more noises and reserve more details, and the efficiency of our approach is better than other traditional de-noising approaches.
independent component analysis de-noising dynamic fuzzy optimize
Guangming Zhang Xuefeng Xian Zhiming Cui Jian Wu
The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006, China
国际会议
2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)
太原
英文
209-212
2009-07-10（万方平台首次上网日期，不代表论文的发表时间）