Computer aided ultrasound laboratory

V. Cerný, R. Zajac
{"title":"Computer aided ultrasound laboratory","authors":"V. Cerný, R. Zajac","doi":"10.1109/CBMS.1997.596403","DOIUrl":null,"url":null,"abstract":"A computer support system works in our sonography laboratory providing essential services like the data-base for medical findings combined with the image data-base. The system is based on the digitization of the video-signal output of the sonograph. We have studied the possibility of easing various picture processing methods to further support the diagnosis-making process. We report here on our experience with three particular methods. So far the most successful method we have developed was the quasi-tomographical processing of images. We take up to 16 scans of the same section plane from different positions of the ultrasonic probe. The final image is obtained as a suitable average of the matched images. The method proved to be very helpful in the diagnostic process and is used on an everyday basis. On a somewhat more academic level we are studying the use of simple neural net classifiers to evaluate textural content in the images. The nets are trained on sets of texture patterns and then used to classify the testing samples. We present here two particular examples: classifying malignant tissue in testes and segmentation (sinus vs parenchyma) in kidney. Our experience with these methods is still limited. However, our conjecture is that the texture information can be used as a supportive tool in clinical praxis.","PeriodicalId":292377,"journal":{"name":"Proceedings of Computer Based Medical Systems","volume":"262 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Computer Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.1997.596403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

A computer support system works in our sonography laboratory providing essential services like the data-base for medical findings combined with the image data-base. The system is based on the digitization of the video-signal output of the sonograph. We have studied the possibility of easing various picture processing methods to further support the diagnosis-making process. We report here on our experience with three particular methods. So far the most successful method we have developed was the quasi-tomographical processing of images. We take up to 16 scans of the same section plane from different positions of the ultrasonic probe. The final image is obtained as a suitable average of the matched images. The method proved to be very helpful in the diagnostic process and is used on an everyday basis. On a somewhat more academic level we are studying the use of simple neural net classifiers to evaluate textural content in the images. The nets are trained on sets of texture patterns and then used to classify the testing samples. We present here two particular examples: classifying malignant tissue in testes and segmentation (sinus vs parenchyma) in kidney. Our experience with these methods is still limited. However, our conjecture is that the texture information can be used as a supportive tool in clinical praxis.
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
计算机辅助超声实验室
我们的超声实验室设有计算机辅助系统,提供医学结果数据库和图像数据库等基本服务。该系统是基于超声仪的视频信号输出的数字化。我们研究了简化各种图像处理方法的可能性,以进一步支持诊断过程。我们在这里报告我们使用三种特殊方法的经验。到目前为止,我们开发的最成功的方法是图像的准层析处理。我们从超声探头的不同位置对同一切面进行多达16次扫描。最终图像作为匹配图像的合适平均值得到。该方法在诊断过程中被证明是非常有用的,并在日常基础上使用。在更学术的层面上,我们正在研究使用简单的神经网络分类器来评估图像中的纹理内容。这些网络在纹理模式集上进行训练,然后用于对测试样本进行分类。我们在这里提出两个特别的例子:睾丸恶性组织的分类和肾脏恶性组织的分割(窦性与实质)。我们使用这些方法的经验仍然有限。然而,我们的猜想是纹理信息可以用作临床实践的辅助工具。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Computer aided ultrasound laboratory Diagnosis of sport injuries with machine learning: explanation of induced decisions Multichannel ECG measurement system Knowledge-based mechanical imaging Detection of human reflex response in EMG signals: a time-frequency approach
×
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