ImageJ as an extensible image processing framework

ImageJ 作为可扩展的图像处理框架

• 批准号: 7939813
• 负责人: Kevin William Eliceiri
• 金额: $ 89.35万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7939813
• 关键词: Adoption; Algorithms; Architecture; Area; Biological; Biological Sciences; Biology; Biomedical Research; Code; Communities; Complex; Computer software; Coupled; Data; Databases; Development; Disease; Ensure; Fostering; Foundations; Growth; Image; Image Analysis; Imagery; Language; Lead; Libraries; Link; Location; Measurement; Metadata; Modeling; Output; Play; Plug-in; Process; Public Domains; Quantitative Microscopy; Research; Research Personnel; Resolution; Role; Solid; Source; Source Code; System; Systems Integration; Testing; Time; United States National Institutes of Health; Work; Writing; base; cluster computing; data modeling; design; flexibility; image processing; improved; interoperability; method development; open source; programs; public health relevance; repository; research study; statistics; tool

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.

描述（由申请人提供）：成像是现代生物学家可用的最强大工具之一，定量显微镜和图像分析的最新进展极大地加速了我们对基本生物学和生物医学研究中许多复杂和动态过程的理解。尽管商业和封闭式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，方法开发和部署向多样化的受众群体。公共领域图像分析程序“ ImageJ”是由美国国立卫生研究院（National Institutes of Health）韦恩·拉斯（Wayne Rasband）维护和开发的，是一种广泛使用的工具，用于生物科学中的图像分析。由于其易用性，灵活的脚本语言和插件架构，ImageJ发现自己已被非程序员，业余程序员和专业程序员有效地使用。但是，在持续增长的一段时间并在该计划的原始意图范围之外添加功能之后，任何成功的软件项目都受益于随后的审查和重构时期，ImageJ也不例外。此类审查有助于该计划可让新移民访问，足以适合专家，并且与不断发展的社区相关。由于ImageJ的限制，我们不受阻碍地加入社区的研究人员的紧迫需求，这使我们提出了三个最大收益的目标：AIM I-改进ImageJ核心架构改进的核心体系结构需要与ImageJ项目的开发和稳定性相互稳定性，以及它与其他软件的互动性以及支持其他功能和支持新功能和应用程序的能力。这将涉及（a）将数据模型与用户界面分开，（b）引入算法的扩展框架，以及（c）扩展图像数据模型。 AIM II-通过将ImageJ与现有的开源程序连接起来，以确保开发朝着最大化互操作性的实用方向进行扩展功能，我们将将改进的ImageJ框架与两个现有的开源生物学应用程序（Visbio（Visbio）（多维可视化）和蜂窝式（对象识别和测量）相连。这些将提供ImageJ改进的功能，并作为其他寻求利用ImageJ的软件的示例。 AIM III-在保持兼容性ImageJ的同时，发展社区驱动的开发具有强大的，已建立的用户群，其中成千上万的插件和宏设计用于执行各种任务。因此，对ImageJ平台的鲁ck更改可能会破坏现有代码并驱逐现有用户。为了促进一个不断增长的社区的参与，理解和热情，我们建议采用几种“最佳实践”，以与其他现代，成功的开源项目一致，该项目将共同基于Imagej的扎实社区驱动的发展基础。 公共卫生相关性：成像是现代生物学家可用的最强大工具之一，定量显微镜和图像分析的最新进展极大地加速了我们对许多复杂而动态疾病过程的理解。虽然商业和封闭的源程序将始终在图像分析中起关键作用，但新算法以及方法开发以及向多样化受众进行生物学和生物医学研究的方法，需要将ImageJ作为公共领域成像处理工具的持续开发。

项目成果

Biological imaging software tools.

• DOI: 10.1038/nmeth.2084
• 发表时间: 2012-06-28
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Eliceiri, Kevin W.;Berthold, Michael R.;Goldberg, Ilya G.;Ibanez, Luis;Manjunath, B. S.;Martone, Maryann E.;Murphy, Robert F.;Peng, Hanchuan;Plant, Anne L.;Roysam, Badrinath;Stuurmann, Nico;Swedlow, Jason R.;Tomancak, Pavel;Carpenter, Anne E.
• 通讯作者: Carpenter, Anne E.

ImageJ2: ImageJ for the next generation of scientific image data.

• DOI: 10.1186/s12859-017-1934-z
• 发表时间: 2017-11-29
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Rueden CT;Schindelin J;Hiner MC;DeZonia BE;Walter AE;Arena ET;Eliceiri KW
• 通讯作者: Eliceiri KW

NIH Image to ImageJ: 25 years of image analysis.

• DOI: 10.1038/nmeth.2089
• 发表时间: 2012-07
• 期刊: Nature methods
• 影响因子: 48
• 作者: Schneider CA;Rasband WS;Eliceiri KW
• 通讯作者: Eliceiri KW