Medical Image De-noising Extended 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.