陈. C. Xing, 宋智洋 Song Zhi-yang, 周明全 Zhou Ming-quan, 武仲科 Wu Zhong-ke, 王醒策 Wang Xing-ce
{"title":"An improved non-local mean filter filtering algorithm facing the cerebrovascular segmentation","authors":"陈. C. Xing, 宋智洋 Song Zhi-yang, 周明全 Zhou Ming-quan, 武仲科 Wu Zhong-ke, 王醒策 Wang Xing-ce","doi":"10.3788/CO.20140704.0572","DOIUrl":null,"url":null,"abstract":"We introduce the classical non-local means filtering algorithm and the improved non-local means filtering algorithm with the weight function modified by Manjon. In this paper,we propose different weight function,and make it have rotating shift invariance for the local windows while keeping the time complexity of optimizing the visual effect and SNR. By adding noise standard deviation from Gaussian additive noise ranging from 10 to 100,we compare the improved algorithms with traditional filtering algorithms and Manjon non-mean filtering algorithm. The results show that the improved algorithm from either visual or numerical is superior to Manjon non-mean filtering algorithm.","PeriodicalId":10133,"journal":{"name":"Chinese Journal of Optics and Applied Optics","volume":"135 1","pages":"572-580"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Optics and Applied Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3788/CO.20140704.0572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We introduce the classical non-local means filtering algorithm and the improved non-local means filtering algorithm with the weight function modified by Manjon. In this paper,we propose different weight function,and make it have rotating shift invariance for the local windows while keeping the time complexity of optimizing the visual effect and SNR. By adding noise standard deviation from Gaussian additive noise ranging from 10 to 100,we compare the improved algorithms with traditional filtering algorithms and Manjon non-mean filtering algorithm. The results show that the improved algorithm from either visual or numerical is superior to Manjon non-mean filtering algorithm.