ImageJ for the Next Generation of Scientific Image Data

By Curtis T. Rueden, Kevin W. Eliceiri. Microscopy and Microanalysis, 2019

500 citations recorded by the source corpus.

Quantifying images is now a critical, widespread need in biological research as light microscopy experiments continue to grow in scale, size, dimensionality, scope, modality, and complexity. It is becoming clear that given the complexity, and heterogeneity of the modern image dataset, there cannot be a single software solution. Different imaging processing and visualization approaches need access not only to the data but also to each other. There needs to be compatibility not only in file import and export but interoperability in preserving and communicating what was done to the image. There is a great opportunity in achieving this interoperability, tools that can talk to each other not only enable new biological discovery but also efficiencies in sharing code and in many cases more precise workflows. We present our efforts towards interoperability and extensibility in the ImageJ consortium. We are actively developing key software libraries like ImgLib and ImageJ Ops that are utilized to analyze and visualize biological image data, to the developmental benefit of not only of the applications but the libraries themselves. We also overview the two major development efforts of the ImageJ [1,2] family of image analysis, FIJI [3] and ImageJ2 [4].

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/029d03654fb0aff7f9e03083284dc04c741c9ab2

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Citations in the corpus

Root anatomy and soil resource capture (2021, methodology): “Traditionally, ImageJ—an open-source platform (Schindelin et al. 2012; Rueden et al. 2017; Rasband 2015; http:// imagej. net/), has been extensively used for manual and batch automation routines for image analysis via native macros or developing new plugins (Arena et al. 2017).”