人工神经网络在显微图像微泡自动测量中的应用

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems Pub Date : 1992-06-14 DOI:10.1109/CBMS.1992.244957

Baoning Pan, K. Abdelhamied

{"title":"人工神经网络在显微图像微泡自动测量中的应用","authors":"Baoning Pan, K. Abdelhamied","doi":"10.1109/CBMS.1992.244957","DOIUrl":null,"url":null,"abstract":"A novel approach for quantitative segmentation and measurement of oxygen microbubbles in microscopic images is presented. In this approach, ellipse-based models were first built using moment parameters as rough approximations of oxygen microbubbles. Artificial neural networks were then developed and trained for segmentation refinement. The results show that the proposed approach achieved high accuracy of microbubbles measurement with less than 8% measurement error.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"77 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of artificial neural networks for automatic measurement of micro-bubbles in microscopic images\",\"authors\":\"Baoning Pan, K. Abdelhamied\",\"doi\":\"10.1109/CBMS.1992.244957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel approach for quantitative segmentation and measurement of oxygen microbubbles in microscopic images is presented. In this approach, ellipse-based models were first built using moment parameters as rough approximations of oxygen microbubbles. Artificial neural networks were then developed and trained for segmentation refinement. The results show that the proposed approach achieved high accuracy of microbubbles measurement with less than 8% measurement error.<<ETX>>\",\"PeriodicalId\":197891,\"journal\":{\"name\":\"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems\",\"volume\":\"77 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.1992.244957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.1992.244957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

提出了一种新的显微图像中氧微泡的定量分割和测量方法。在这种方法中，首先建立基于椭圆的模型，使用力矩参数作为氧微泡的粗略近似。然后开发并训练人工神经网络进行分割细化。结果表明，该方法测量精度高，测量误差小于8%

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Application of artificial neural networks for automatic measurement of micro-bubbles in microscopic images

A novel approach for quantitative segmentation and measurement of oxygen microbubbles in microscopic images is presented. In this approach, ellipse-based models were first built using moment parameters as rough approximations of oxygen microbubbles. Artificial neural networks were then developed and trained for segmentation refinement. The results show that the proposed approach achieved high accuracy of microbubbles measurement with less than 8% measurement error.<>

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

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems

自引率

0.00%

发文量