CT Image De-Noising Using Wavelet Transform and Dynamic Fuzzy Logic

Dynamic fuzzy logic (DFL) is given to solve dynamic fuzzy data problems. Dynamic fuzzy data exists universally, especially in the domain of medical image processing performance evaluation. This paper proposes a new evaluation model for CT medical image de-noising, which is using wavelet transform and dynamic fuzzy logic. Firstly, the CT medical image was decomposed by wavelet transform to obtain the different wavelet coefficients in different level. Then dynamic fuzzy logic theory was applied to construct a series of adaptive membership functions. At last, these membership functions were applied to optimize the coefficients distribution for image reconstruction. By applying this model, the selection of wavelet coefficients 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.