Quantitative Comparison of Two Particle Tracking Methods in Fluorescence Microscopy Images

Matsilele Mabaso, Bhekisipho Twala, D. Withey
{"title":"Quantitative Comparison of Two Particle Tracking Methods in Fluorescence Microscopy Images","authors":"Matsilele Mabaso, Bhekisipho Twala, D. Withey","doi":"10.1109/BRICS-CCI-CBIC.2013.106","DOIUrl":null,"url":null,"abstract":"Tracking of multiple bright particles (spots) in fluorescence microscopy image sequences is seen as a crucial step in understanding complex information in the cell. However, fluorescence microscopy generates high a volume of noisy image data that cannot be analysed efficiently by means of manual analysis. In this study we compare the performance of two computer-based tracking methods for tracking of bright particles in fluorescence microscopy image sequences. The methods under comparison are, Interacting Multiple Model filter and Feature Point Tracking. The performance of the methods is validated using synthetic but realistic image sequences and real images. The results from experiments show that the Interacting Multiple Model filter performed best, under the test conditions.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Tracking of multiple bright particles (spots) in fluorescence microscopy image sequences is seen as a crucial step in understanding complex information in the cell. However, fluorescence microscopy generates high a volume of noisy image data that cannot be analysed efficiently by means of manual analysis. In this study we compare the performance of two computer-based tracking methods for tracking of bright particles in fluorescence microscopy image sequences. The methods under comparison are, Interacting Multiple Model filter and Feature Point Tracking. The performance of the methods is validated using synthetic but realistic image sequences and real images. The results from experiments show that the Interacting Multiple Model filter performed best, under the test conditions.
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
荧光显微镜图像中两种粒子跟踪方法的定量比较
在荧光显微镜图像序列中跟踪多个明亮的颗粒(斑点)被认为是理解细胞中复杂信息的关键步骤。然而,荧光显微镜产生大量的噪声图像数据,无法通过人工分析有效地分析。在这项研究中,我们比较了两种基于计算机的跟踪方法的性能,用于跟踪荧光显微镜图像序列中的明亮颗粒。比较的方法有:交互多模型滤波和特征点跟踪。用合成但真实的图像序列和真实图像验证了方法的性能。实验结果表明,在测试条件下,交互多模型滤波器的性能最好。
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 Simulations of Small Societies Under Social Transfer Systems Modeling of Reasoning in Intelligent Systems by Means of Integration of Methods Based on Case-Based Reasoning and Inductive Notions Formation Bi-dimensional Neural Equalizer Applied to Optical Receiver A New Algorithm Based on Differential Evolution for Combinatorial Optimization A Cooperative Parallel Particle Swarm Optimization for High-Dimension Problems on GPUs
×
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