A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion

With widely availed clinically used radionuclides,Cer enkov luminescence imaging(CLI)has become a potential tool in the field of optical molecular imaging.However,the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly,which affects the acauracy of quantitative analysis,as well as the three dimensional reconstruction.In this work,a novel denoising framework based on fuzzy dlustering and curvat ure driven difusion(CDD)is proposed to remove this kind of impulse noises.To improve the accuracy,the F u1zzy Local Information C-Means algorithm,where spatial information is evolved,is used.We evaluate the per formance of the proposed framework sys-tematically with a series of experiments,and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method.We hope this work may provide a useful data pre processing tool for CLI and its following studies.