Laboratory for Optical and Computational Instrumentation (LOCI), Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin.
Department of Biomedical Engineering, University of Wisconsin at Madison, Madison, Wisconsin.
Curr Protoc. 2021 Aug;1(8):e204. doi: 10.1002/cpz1.204.
ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution.
ImageJ 为跨科学领域的图像处理提供了一个框架，同时它也是完全开源的。多年来，ImageJ 已经得到了实质性的扩展，以支持新兴科学成像领域的新应用，特别是在生物显微镜领域，通过 Fiji 对 ImageJ 的分发，其功能变得更加易于使用。在这个软件生态系统中，已经做了很多工作来扩展 ImageJ 的可访问性，以便在各种编程场景中使用脚本、宏和插件，例如在 Groovy 和 Python 中，以及在 Jupyter 笔记本和云计算中。我们提供了五个协议，展示了 ImageJ 在图像处理各种工作流程中的可扩展性。我们首先关注荧光寿命成像显微镜 (FLIM) 数据，因为这需要大量的处理才能提供对细胞微环境的定量见解。其次，我们展示了如何现在可以利用 ImageJ 进行常见的图像处理技术，特别是图像去卷积和反转，同时突出显示 ImageJ 的新内置功能-特别是其完全无头运行的能力和 Ops 匹配功能，该功能为给定的函数和数据输入选择最佳算法，从而实现处理速度的提升。总的来说，这些协议可以用作自动化生物图像处理工作流程的基础。© 2021 威立出版社有限责任公司。基础协议 1：使用 PyImageJ 进行 FLIM 数据处理可选协议：在 Jupyter 笔记本中使用 Groovy FLIMJ 基础协议 2：使用 ImageJ Ops 进行图像去卷积支持协议 1：使用 ImageJ Ops 匹配功能进行图像反转支持协议 2：无头 ImageJ 去卷积。
