A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion
作者机构：School of Information Science and Technology Northwest University Xi'anShaanxi 710069P.R.China Department of Nuclear MedicineXijing Hospital Fourth Military Medical University Xi'anShaanxi 710032P.R.China
出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)
年 卷 期：2018年第11卷第4期
页 面：35-42页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
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
主 题：Cerenkov luminescence imaging image processing radionuclide imaging
摘 要：With widely availed clinically used radionuclides,Cer enkov luminescence imaging(CLI)has become a potential tool in the field of optical molecular ***,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 *** this work,a novel denoising framework based on fuzzy dlustering and curvat ure driven difusion(CDD)is proposed to remove this kind of impulse *** improve the accuracy,the F u1zzy Local Information C-Means algorithm,where spatial information is evolved,is *** 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 *** hope this work may provide a useful data pre processing tool for CLI and its following studies.
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