用Python和scikit-image库分析微层析成像数据

Emmanuelle Gouillart, Juan Nunez-Iglesias, Stéfan van der Walt
{"title":"用Python和scikit-image库分析微层析成像数据","authors":"Emmanuelle Gouillart,&nbsp;Juan Nunez-Iglesias,&nbsp;Stéfan van der Walt","doi":"10.1186/s40679-016-0031-0","DOIUrl":null,"url":null,"abstract":"<p>The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. <span>scikit-image</span> is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. <span>scikit-image</span> users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. <span>scikit-image</span> combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.</p>","PeriodicalId":460,"journal":{"name":"Advanced Structural and Chemical Imaging","volume":"2 1","pages":""},"PeriodicalIF":3.5600,"publicationDate":"2016-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40679-016-0031-0","citationCount":"24","resultStr":"{\"title\":\"Analyzing microtomography data with Python and the scikit-image library\",\"authors\":\"Emmanuelle Gouillart,&nbsp;Juan Nunez-Iglesias,&nbsp;Stéfan van der Walt\",\"doi\":\"10.1186/s40679-016-0031-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. <span>scikit-image</span> is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. <span>scikit-image</span> users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. <span>scikit-image</span> combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.</p>\",\"PeriodicalId\":460,\"journal\":{\"name\":\"Advanced Structural and Chemical Imaging\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5600,\"publicationDate\":\"2016-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/s40679-016-0031-0\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Structural and Chemical Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s40679-016-0031-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Structural and Chemical Imaging","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1186/s40679-016-0031-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 24

摘要

图像的探索和处理是许多x射线成像模式的科学工作流程的一个重要方面。用户需要兼具交互性、多功能性和性能的工具。scikit-image是Python语言的开源图像处理工具包,支持多种文件格式,并与2D和3D图像兼容。该工具包公开了一个简单的编程接口,其中的主题模块根据其用途对功能进行分组,例如图像恢复、分割和测量。scikit-image用户受益于丰富的科学Python生态系统,其中包含许多用于可视化或机器学习等任务的强大库。scikit-image结合了温和的学习曲线,通用的图像处理能力,以及高通量x射线成像数据分析所需的可扩展性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Analyzing microtomography data with Python and the scikit-image library

The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit-image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit-image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging 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
Advanced Structural and Chemical Imaging
Advanced Structural and Chemical Imaging Medicine-Radiology, Nuclear Medicine and Imaging
自引率
0.00%
发文量
0
期刊最新文献
Detection of defects in atomic-resolution images of materials using cycle analysis Imaging of polymer:fullerene bulk-heterojunctions in a scanning electron microscope: methodology aspects and nanomorphology by correlative SEM and STEM mpfit: a robust method for fitting atomic resolution images with multiple Gaussian peaks Investigation of hole-free phase plate performance in transmission electron microscopy under different operation conditions by experiments and simulations Optimal principal component analysis of STEM XEDS spectrum images
×
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