Speckle noise reduction for 3D ultrasound images by optimum threshold parameter estimation of bi-dimensional empirical mode decomposition using Fisher discriminant analysis

IF 0.6 Q3 Engineering
Rafid Mostafiz, Mohammad Motiur Rahman, P. K. M. Kumar, Mohammad A. Islam
{"title":"Speckle noise reduction for 3D ultrasound images by optimum threshold parameter estimation of bi-dimensional empirical mode decomposition using Fisher discriminant analysis","authors":"Rafid Mostafiz, Mohammad Motiur Rahman, P. K. M. Kumar, Mohammad A. Islam","doi":"10.1504/IJSISE.2018.10013070","DOIUrl":null,"url":null,"abstract":"This paper presents an approach to speckle noise reduction for 3D ultrasound images using bi-dimensional empirical mode decomposition (BEMD). 3D ultrasound is a popular diagnostic system for assessing the progression of diseases for its diverse benefits and application. Speckle noise often obscures the fine details and degrades the spatial resolution and, contrast quality that makes the interpretation of ultrasound image more difficult. The proposed method estimates an optimum threshold value of intrinsic mode functions (IMFs) using Fisher discriminant analysis (FDA) for reducing the speckles in 3D volume of ultrasound images. FDA has applied on 2D IMFs, then explored and extended to 3D. The 3D volume rendering is performed on the basis of integrating 2D slice images that provide strong speckle reduction and edge preservation. The experiment result has compared with the several other state-of-the-art threshold methods. The proposed method is also good in edge preservation and contrast resolution.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"93"},"PeriodicalIF":0.6000,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Signal and Imaging Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJSISE.2018.10013070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 2

Abstract

This paper presents an approach to speckle noise reduction for 3D ultrasound images using bi-dimensional empirical mode decomposition (BEMD). 3D ultrasound is a popular diagnostic system for assessing the progression of diseases for its diverse benefits and application. Speckle noise often obscures the fine details and degrades the spatial resolution and, contrast quality that makes the interpretation of ultrasound image more difficult. The proposed method estimates an optimum threshold value of intrinsic mode functions (IMFs) using Fisher discriminant analysis (FDA) for reducing the speckles in 3D volume of ultrasound images. FDA has applied on 2D IMFs, then explored and extended to 3D. The 3D volume rendering is performed on the basis of integrating 2D slice images that provide strong speckle reduction and edge preservation. The experiment result has compared with the several other state-of-the-art threshold methods. The proposed method is also good in edge preservation and contrast resolution.
基于Fisher判别分析的二维经验模态分解最优阈值参数估计对三维超声图像进行斑点降噪
提出了一种基于二维经验模态分解(BEMD)的三维超声图像散斑降噪方法。三维超声是一种流行的诊断系统评估疾病的进展,其多种益处和应用。斑点噪声往往模糊了精细的细节,降低了空间分辨率和对比度质量,使超声图像的解释更加困难。提出了一种利用Fisher判别分析(FDA)估计固有模态函数(IMFs)的最佳阈值的方法,用于减少超声图像三维体积中的斑点。FDA先应用于二维IMFs,然后探索并扩展到三维。三维体绘制是在整合二维切片图像的基础上进行的,这些图像提供了强大的斑点减少和边缘保留。实验结果与其他几种最先进的阈值方法进行了比较。该方法具有良好的边缘保持和对比度分辨率。
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
CiteScore
2.10
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