ImageJ2: ImageJ for the next generation of scientific image data.
ImageJ2: ImageJ for the next generation of scientific image data.
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DOI：
10.1186/s12859-017-1934-z
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发表时间：
2017-11-29
影响因子：
3
通讯作者：
Eliceiri KW
中科院分区：
文献类型：
--
作者：
Rueden CT;Schindelin J;Hiner MC;DeZonia BE;Walter AE;Arena ET;Eliceiri KW
ImageJ是一个图像分析程序，广泛应用于生物科学及其他领域。由于其易用性，可记录的宏语言和可扩展的插件架构，ImageJ享有非程序员，业余程序员和专业开发人员的贡献。使这样的贡献者的多样性导致了一个跨越生物和物理科学的大型社区。然而，快速增长的用户群，不同的插件套件和技术限制已经揭示了一个明确的需要协调一致的软件工程的努力，以支持新兴的成像范例，以确保软件的能力，以处理现代科学的要求。我们重写了整个ImageJ代码库，设计了一个重新设计的插件机制，旨在促进各个级别的可扩展性，目标是创建一个更强大的工具，继续为现有社区服务，同时满足更广泛的科学需求。这个下一代ImageJ，在区别重要的地方称为“ImageJ 2”，提供了许多新功能。它分离了关注点，将数据模型与用户界面完全解耦。它强调与外部应用程序的集成，以最大限度地提高互操作性。它强大的新插件框架允许从图像格式到脚本语言，再到可视化的一切都可以由社区扩展。重新设计的数据模型支持任意大的N维数据集，这在现代图像采集中越来越常见。尽管这些变化的范围很大，但仍保持了向后兼容性，因此这种新功能可以与经典的ImageJ接口无缝集成，允许用户和开发人员按照自己的节奏迁移到这些新方法。科学成像受益于开源项目，这些项目推动了新方法的开发和部署，面向不同的受众。ImageJ一直在不断发展，但新的和新兴的科学要求也为ImageJ的发展提出了相应的挑战。所描述的改进提供了一个灵活的框架，旨在支持这些要求以及适应未来的需求。未来的工作将集中在在这个框架中实现新的算法，并扩大与其他流行的科学软件套件的合作。本文的在线版本（doi：10.1186/s12859-017-1934-z）包含补充材料，可供授权用户使用。
ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.