FluoRender:神经生物学研究中三维和四维共聚焦显微镜数据可视化的二维图像空间方法应用。

Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen
{"title":"FluoRender:神经生物学研究中三维和四维共聚焦显微镜数据可视化的二维图像空间方法应用。","authors":"Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen","doi":"10.1109/pacificvis.2012.6183592","DOIUrl":null,"url":null,"abstract":"<p><p>2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.</p>","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":" ","pages":"201-208"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622106/pdf/nihms370292.pdf","citationCount":"0","resultStr":"{\"title\":\"FluoRender: An Application of 2D Image Space Methods for 3D and 4D Confocal Microscopy Data Visualization in Neurobiology Research.\",\"authors\":\"Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen\",\"doi\":\"10.1109/pacificvis.2012.6183592\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.</p>\",\"PeriodicalId\":73302,\"journal\":{\"name\":\"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium\",\"volume\":\" \",\"pages\":\"201-208\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622106/pdf/nihms370292.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/pacificvis.2012.6183592\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/pacificvis.2012.6183592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

二维图像空间方法是在将体积数据投影和渲染到二维图像空间后应用的处理方法,如二维滤波、色调映射和合成。在体积可视化应用领域,大多数二维图像空间方法都能比其三维对应方法更有效地执行。最重要的是,当二维图像空间方法与体积渲染方法一起应用时,可用于提高体积可视化质量。在本文中,我们介绍并讨论了一系列二维图像空间方法在共聚焦显微可视化中的应用,包括二维色调映射、二维合成和二维色彩映射。这些方法很容易与我们现有的共聚焦可视化工具 FluoRender 相集成,从而形成一个功能齐全的可视化系统,满足神经生物学家对共聚焦显微镜数据进行定性分析的需求。
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
FluoRender: An Application of 2D Image Space Methods for 3D and 4D Confocal Microscopy Data Visualization in Neurobiology Research.

2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.

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
A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care. Keynote speaker: Changing the world with visual analytics Keynote speaker: Requirements and recent directions in augmented reality visualization Chair message Keynote speaker
×
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