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

作　　者：Xin Cao Yi Sun Fei Kang Lin Wang Huangjian Yi Fengjun Zhao Linzhi Su Xiaowei He 

机构地区：[1]School of Information Science and Technology Northwest University Xi'an,Shaanxi 710069,P.R.China [2]Department of Nuclear Medicine,Xijing Hospital Fourth Military Medical University Xi'an,Shaanxi 710032,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2018年第4期35-42,共8页创新光学健康科学杂志（英文）

基　　金：the Program of the National Natural Science Foundation of China under Grant Nos.61701403,61601363,11571012,61372046 and 61640418;the Natural Science Basic Research Plan in Shaanxi Province of China under Grant Nos.2017JQ6006 and 2017JQ6017.

摘　　要：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.

关 键 词：Cerenkov luminescence imaging image processing radionuclide imaging 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]