利用近似信息传递从压缩测量中重建超声图像

J. Kim, A. Basarab, P. Hill, D. Bull, D. Kouamé, A. Achim
{"title":"利用近似信息传递从压缩测量中重建超声图像","authors":"J. Kim, A. Basarab, P. Hill, D. Bull, D. Kouamé, A. Achim","doi":"10.1109/EUSIPCO.2016.7760310","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. We compare the results with existing reconstruction algorithms already used in ultrasound imaging and quantify the performance improvement.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ultrasound image reconstruction from compressed measurements using approximate message passing\",\"authors\":\"J. Kim, A. Basarab, P. Hill, D. Bull, D. Kouamé, A. Achim\",\"doi\":\"10.1109/EUSIPCO.2016.7760310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. We compare the results with existing reconstruction algorithms already used in ultrasound imaging and quantify the performance improvement.\",\"PeriodicalId\":127068,\"journal\":{\"name\":\"2016 24th European Signal Processing Conference (EUSIPCO)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 24th European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EUSIPCO.2016.7760310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 24th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUSIPCO.2016.7760310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

本文提出了一种基于近似消息传递(AMP)算法的生物医学超声图像压缩采样重构新框架。AMP是一种迭代算法,它通过压缩采样框架内的图像去噪来执行图像重建。在这项工作中,我们的目的是评估降噪和变换域的几种组合的优点,这是决定恢复性能的两个主要因素。特别地,我们研究了空间域、DCT域和小波域的重建性能。我们将结果与已经用于超声成像的现有重建算法进行比较,并量化性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultrasound image reconstruction from compressed measurements using approximate message passing
In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. We compare the results with existing reconstruction algorithms already used in ultrasound imaging and quantify the performance improvement.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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