The ImageJ ecosystem: An open platform for biomedical image analysis.
The ImageJ ecosystem: An open platform for biomedical image analysis.
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DOI：
10.1002/mrd.22489
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发表时间：
2015-07
影响因子：
2.5
通讯作者：
Eliceiri KW
中科院分区：
文献类型：
--
作者：
Schindelin J;Rueden CT;Hiner MC;Eliceiri KW
显微镜技术发展迅速，使得定量生物医学成像越来越经济实惠、快速且精确，这就相应地需要更先进的图像处理和分析技术。有各种各样的软件可供选择——从商业软件到学术软件，从专用软件到多功能软件，从小型软件到大型软件——但适合科学研究的软件的一个关键特征是其可获取性。开源软件是科学研究的理想选择，因为它可以被自由检查、修改和重新分发；特别是开源软件平台ImageJ对生命科学产生了巨大影响，并将继续如此。从一开始，ImageJ就有了显著的发展，这主要得益于它的免费使用以及活跃且乐于助人的用户群体。从感兴趣的业余爱好者、技术助理、学生、科研人员到高级生物学研究人员等各种各样的科学家每天都在使用ImageJ，并通过其专用的邮件列表交流知识。ImageJ的用途从数据可视化和教学到高级图像处理和统计分析不等。该软件的可扩展性不断吸引着各个职业阶段的生物学家以及希望有效实现特定图像处理算法的计算机科学家。在这篇综述中，我们将ImageJ项目作为一个案例研究，探讨开源软件如何培育其软件工具套件，使科学界能够轻松获取大量的图像分析技术。我们特别探讨了ImageJ如此受欢迎的原因、它对生命科学的影响、它如何启发其他项目以及它如何受到ImageJ生态系统内协同进化项目的自身影响。
Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available – from commercial to academic, special-purpose to Swiss army knife, small to large–but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts life science, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem.