MRI denoising based on neutrosophic wiener filtering

J. Mohan, Yanhui Guo, V. Krishnaveni, Kanchana Jeganathan
{"title":"MRI denoising based on neutrosophic wiener filtering","authors":"J. Mohan, Yanhui Guo, V. Krishnaveni, Kanchana Jeganathan","doi":"10.1109/IST.2012.6295518","DOIUrl":null,"url":null,"abstract":"In this paper, a new filtering method is presents to remove Rician noise from magnetic resonance image. This filter is based on Neutrosophic set (NS) approach of wiener filtering. A Neutrosophic Set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated MR images from Brainweb database with Rician noise added. The visual and the diagnostic quality of the denoised image are well preserved. The performance of this filter is compared with anisotropic diffusion filter (ADF) and unbiased non local mean filter (UNLM).","PeriodicalId":213330,"journal":{"name":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2012.6295518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28

Abstract

In this paper, a new filtering method is presents to remove Rician noise from magnetic resonance image. This filter is based on Neutrosophic set (NS) approach of wiener filtering. A Neutrosophic Set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated MR images from Brainweb database with Rician noise added. The visual and the diagnostic quality of the denoised image are well preserved. The performance of this filter is compared with anisotropic diffusion filter (ADF) and unbiased non local mean filter (UNLM).
基于中性维纳滤波的MRI去噪
本文提出了一种新的滤波方法来去除磁共振图像中的噪声。该滤波器基于维纳滤波的中性集(NS)方法。中性集(Neutrosophic Set, NS)是中性理论的一部分,研究中性物的起源、性质和范围,以及它们与不同概念谱的相互作用。现在,我们将嗜中性集应用到图像域,定义一些图像去噪的概念和算子。将图像转换为NS域,使用三个隶属集:True (T), Indeterminacy (I)和False (F)进行描述。定义中性集的熵并使用它来度量不确定性。对T和F进行了ω-维纳滤波运算,降低了集合不确定性,消除了噪声。在Brainweb数据库的模拟MR图像上加入了噪声,并进行了实验。去噪后图像的视觉和诊断质量得到很好的保留。将该滤波器的性能与各向异性扩散滤波器(ADF)和无偏非局部平均滤波器(UNLM)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信