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Summary: ImageJ-MATLAB is a lightweight Java library facilitating bi-directional interoperability between MATLAB and Image J. By defining a standard for translation between matrix and image data structures, researchers are empowered to select the best tool for their image-analysis tasks. [ABSTRACT FROM AUTHOR]

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many "bridge" layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future., Competing Interests: Authors DK, MW, CB, SH, AW, and MB were employed by KNIME GmbH. Author JS was employed by Microsoft Corporation. Author BN was employed by True North Intelligent Algorithms. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Selzer, Rueden, Hiner, Evans, Kolb, Wiedenmann, Birkhold, Buchholz, Helfrich, Northan, Walter, Schindelin, Pietzsch, Saalfeld, Berthold and Eliceiri.)

Modern biological research particularly in the fields of developmental and cell biology has been transformed by the rapid evolution of the light microscope. The light microscope, long a mainstay of the experimental biologist, is now used for a wide array of biological experimental scenarios and sample types. Much of the great developments in advanced biological imaging have been driven by the digital imaging revolution with powerful processors and algorithms. In particular, this combination of advanced imaging and computational analysis has resulted in the drive of the modern biologist to not only visually inspect dynamic phenomena, but to quantify the involved processes. This need to quantitate images has become a major thrust within the bioimaging community and requires extensible and accessible image processing routines with corresponding intuitive software packages. Novel algorithms both made specifically for light microscopy or adapted from other fields, such as astronomy, are available to biologists, but often in a form that is inaccessible for a number of reasons ranging from data input issues, usability and training concerns, and accessibility and output limitations. The biological community has responded to this need by developing open source software packages that are freely available and provide access to image processing routines. One of the most prominent is the open-source image package ImageJ. In this review, we give an overview of prominent imaging processing approaches in ImageJ that we think are of particular interest for biological imaging and that illustrate the functionality of ImageJ and other open source image analysis software. WIREs Dev Biol 2017, 6:e260. doi: 10.1002/wdev.260 For further resources related to this article, please visit the WIREs website., (© 2016 Wiley Periodicals, Inc.)