超声成像中的多重分形组织表征

E. Villain, H. Wendt, A. Basarab, D. Kouamé
{"title":"超声成像中的多重分形组织表征","authors":"E. Villain, H. Wendt, A. Basarab, D. Kouamé","doi":"10.1109/ISBI.2019.8759404","DOIUrl":null,"url":null,"abstract":"Tissue characterization based on ultrasound (US) images is an extensively explored research field. Most of the existing techniques are focused on the estimation of statistical or acoustic parameters from the backscattered radio-frequency signals, thus complementing the visual inspection of the conventional B-mode images. Additionally, a few studies show the interest of analyzing the fractal or multifractal behavior of human tissues, in particular of tumors. While biological experiments sustain such multifractal behaviors, the observations on US images are rather empirical. To our knowledge, there is no theoretical or practical study relating the fractal or multifractal parameters extracted from US images to those of the imaged tissues. The aim of this paper is to investigate how multifractal properties of a tissue correlate with the ones estimated from a simulated US image for the same tissue. To this end, an original simulation pipeline of multifractal tissues and their corresponding US images is proposed. Simulation results are compared to those in an in vivo experiment.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"14 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"On Multifractal Tissue Characterization in Ultrasound Imaging\",\"authors\":\"E. Villain, H. Wendt, A. Basarab, D. Kouamé\",\"doi\":\"10.1109/ISBI.2019.8759404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Tissue characterization based on ultrasound (US) images is an extensively explored research field. Most of the existing techniques are focused on the estimation of statistical or acoustic parameters from the backscattered radio-frequency signals, thus complementing the visual inspection of the conventional B-mode images. Additionally, a few studies show the interest of analyzing the fractal or multifractal behavior of human tissues, in particular of tumors. While biological experiments sustain such multifractal behaviors, the observations on US images are rather empirical. To our knowledge, there is no theoretical or practical study relating the fractal or multifractal parameters extracted from US images to those of the imaged tissues. The aim of this paper is to investigate how multifractal properties of a tissue correlate with the ones estimated from a simulated US image for the same tissue. To this end, an original simulation pipeline of multifractal tissues and their corresponding US images is proposed. Simulation results are compared to those in an in vivo experiment.\",\"PeriodicalId\":119935,\"journal\":{\"name\":\"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)\",\"volume\":\"14 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI.2019.8759404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2019.8759404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

基于超声图像的组织表征是一个被广泛探索的研究领域。现有的大多数技术都集中在从反向散射射频信号中估计统计或声学参数,从而补充了传统b模式图像的视觉检测。此外,一些研究显示出对分析人体组织,特别是肿瘤的分形或多重分形行为的兴趣。虽然生物实验支持这种多重分形行为,但对美国图像的观察却是经验主义的。据我们所知,从US图像中提取的分形或多重分形参数与成像组织的分形参数之间没有理论或实践研究。本文的目的是研究组织的多重分形特性如何与从模拟的美国图像估计的组织相关联。为此,提出了一种原始的多重分形组织模拟流水线及其对应的US图像。仿真结果与体内实验结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
On Multifractal Tissue Characterization in Ultrasound Imaging
Tissue characterization based on ultrasound (US) images is an extensively explored research field. Most of the existing techniques are focused on the estimation of statistical or acoustic parameters from the backscattered radio-frequency signals, thus complementing the visual inspection of the conventional B-mode images. Additionally, a few studies show the interest of analyzing the fractal or multifractal behavior of human tissues, in particular of tumors. While biological experiments sustain such multifractal behaviors, the observations on US images are rather empirical. To our knowledge, there is no theoretical or practical study relating the fractal or multifractal parameters extracted from US images to those of the imaged tissues. The aim of this paper is to investigate how multifractal properties of a tissue correlate with the ones estimated from a simulated US image for the same tissue. To this end, an original simulation pipeline of multifractal tissues and their corresponding US images is proposed. Simulation results are compared to those in an in vivo experiment.
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
期刊最新文献
Regularisation With a Dictionary of Lines for Medical Ultrasound Image Deconvolution On Multifractal Tissue Characterization in Ultrasound Imaging A Deep Learning Approach To Identify MRNA Localization Patterns Deforming Tessellations For The Segmentation Of Cell Aggregates Multi-Shell Diffusion MRI Measures of Brain Aging: A Preliminary Comparison From ADNI3
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1