ImageJ as an extensible image processing framework

描述（申请人提供）：成像是现代生物学家最强大的工具之一，最近在定量显微镜和图像分析方面的进展大大加快了我们对基础生物学和生物医学研究中许多复杂和动态过程的理解。虽然商业和闭源软件程序将始终在图像分析中发挥关键作用，但需要开源程序来推进新算法和方法的开发，并向不同的受众部署。公共领域的图像分析程序“ImageJ”是由美国国立卫生研究院的Wayne Rasband维护和开发的，是生物科学中广泛使用的图像分析工具。由于易于使用、灵活的脚本语言和插件架构，ImageJ被非程序员、业余程序员和专业程序员都有效地使用。然而，任何成功的软件项目，在经过一段时间的持续增长和在程序最初意图范围之外添加功能之后，都会从随后的审查和重构中受益，ImageJ也不例外。这样的审查有助于该项目对新来者保持可访问性，对专家来说足够强大，并与不断发展的社区相关。现有ImageJ社区的迫切需求，以及由于ImageJ的局限性而阻碍加入社区的研究人员的迫切需求，使我们提出了三个最大利益的目标：目标1 -改进ImageJ核心架构核心架构的改进是ImageJ项目的发展和稳定，以及与其他软件的互操作性和支持新功能和应用程序的能力所必需的。这将涉及(a)将数据模型与用户界面分离，(b)引入算法扩展框架，以及(c)扩大图像数据模型。为了确保开发在一个实际的方向上进行，最大限度地提高互操作性，我们将改进的ImageJ框架与两个现有的开源生物学应用程序，VisBio（多维可视化）和CellProfiler（对象识别和测量）进行接口。这将为ImageJ提供改进的功能，并为其他试图类似地利用ImageJ的软件提供示例。ImageJ拥有强大的、成熟的用户基础，有成千上万的插件和宏用于执行各种各样的任务。因此，对ImageJ平台的鲁莽更改可能会破坏现有代码并赶走现有用户。为了促进不断增长的社区的参与、理解和热情，我们建议采用一些与其他现代、成功的开源项目相一致的“最佳实践”，这些实践将共同建立在ImageJ社区驱动开发的坚实基础之上。

DESCRIPTION (provided by applicant): Imaging is one of the most powerful tools available to the modern biologist and recent advances in quantitative microscopy and image analysis have greatly accelerated our understanding of many complex and dynamic processes in basic biological and biomedical research. While commercial and closed-source software programs will always play a key role in image analysis, open-source programs are needed to advance new algorithm and method development and deployment to a diverse audience. The public domain image analysis program "ImageJ," maintained and developed by Wayne Rasband at the National Institutes of Health, is a widely used tool for image analysis in the biological sciences. Due to its ease of use, flexible scripting language and plug-in architecture, ImageJ has found itself being used effectively by the non-programmer, the amateur programmer, and the professional programmer alike. However, any successful software project, after a period of sustained growth and the addition of functionality outside the scope of the program's original intent, benefits from a subsequent period of scrutiny and refactoring, and ImageJ is no exception. Such review helps the program to remain accessible to newcomers, powerful enough for experts, and relevant to an evolving community. The pressing needs of the existing ImageJ community as well as of researchers who are hindered from joining the community due to limitations in ImageJ lead us to propose three aims of maximal benefit: Aim I - Improve the ImageJ core architecture Improvements in core architecture are required for the development and stability of the ImageJ project, as well as its interoperability with other software and its ability to support new features and applications. This will involve (a) Separating the data model from the user interface, (b) Introducing an extensions framework for algorithms, and (c) Broadening the image data model. Aim II - Expand functionality by interfacing ImageJ with existing open-source programs To ensure that development proceeds in a practical direction that maximizes interoperability, we will interface the improved ImageJ framework with two existing open-source biology applications, VisBio (multidimensional visualization) and CellProfiler (object identification and measurement). These will give ImageJ improved functionality and serve as examples for other software seeking to harness ImageJ similarly. Aim III - Grow community-driven development while maintaining compatibility ImageJ has a strong, established user base, with thousands of plugins and macros designed to perform a wide variety of tasks. Consequently, reckless changes to the ImageJ platform may break existing code and drive away existing users. To foster participation, understanding, and enthusiasm from a growing community, we propose the adoption of several "best practices" in line with other modern, successful open-source projects, which when taken together will build on ImageJ's solid foundation of community-driven development. PUBLIC HEALTH RELEVANCE: Imaging is one of the most powerful tools available to the modern biologist and recent advances in quantitative microscopy and image analysis have greatly accelerated our understanding of many complex and dynamic disease processes. While commercial and closed source programs will always play a key role in image analysis, the continued development of ImageJ as a public domain imaging processing tool is needed for new algorithm and method development and deployment to a diverse audience for biological and biomedical research.