CT Image De-noising using Wavelet Transform and Dynamic Fuzz(可编辑)CT Image De-noising using Wavelet Transform and Dynamic Fuzz(可编辑)
CT Image De-noising using Wavelet Transform and
Dynamic Fuzz
Dynamic Fuzzy LogicGuangming Zhang, Jie Xin, Jian Wu, Zhiming CuiThe Institute of Intelligent Information Processing and Applicati...
CT Image De-noising using Wavelet Transform and Dynamic Fuzz(可编辑)
CT Image De-noising using Wavelet Transform and
Dynamic Fuzz
Dynamic Fuzzy LogicGuangming Zhang, Jie Xin, Jian Wu, Zhiming CuiThe Institute of Intelligent Information Processing and ApplicationSoochow UniversitySuzhou 215006, Chinagmwell@126,
szzmcui@Abstract―Dynamic fuzzy logic (DFL) is given to solve dynamicfuzzy data problems. Dynamic fuzzy data exists universally,especially in the domain of medical image processingperformance evaluation. This paper proposes a new evaluationmodel for CT medical image de-noising, which is using wavelettransform and dynamic fuzzy logic. Firstly, the CT medicalimage was decomposed by wavelet transform to obtain thedifferent wavelet coefficients in different level. Then dynamicfuzzy logic theory was applied to construct a series of adaptivemembership functions. At last, these membership functionswere applied to optimize the coefficients distribution for imagereconstruction. By applying this model, the selection of waveletcoefficients could be optimized scientifically and self-
adaptively. By contrast, this approach could remove morenoises and reserve more details, and the efficiency of
ourapproach is better than other traditional
de-noisingapproaches.Keywords-wavelet transform; DFL; de-noising; CT imageI.INTRODUCTIONIn recent years, a great number of developments and aseries of substantial achievements in the domain of fuzzymathematics’ theory research and application, since L. A.Zadeh proposed fuzzy sets in 1965 [1]. However, thesestheories can only help to solve those static problems.Dynamic fuzzy logic as a effective theory to solve dynamicfuzzy problems is widely researched. In real world, dynamicfuzzy problems exist universally, especially in the
domain ofimage processing. For example, these images becomesmoother and smoother. The word “become” reflects
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