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ImageJ2: ImageJ for the next generation of scientific image data

BACKGROUND: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. En...

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Autores principales: Rueden, Curtis T., Schindelin, Johannes, Hiner, Mark C., DeZonia, Barry E., Walter, Alison E., Arena, Ellen T., Eliceiri, Kevin W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5708080/
https://www.ncbi.nlm.nih.gov/pubmed/29187165
http://dx.doi.org/10.1186/s12859-017-1934-z
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author Rueden, Curtis T.
Schindelin, Johannes
Hiner, Mark C.
DeZonia, Barry E.
Walter, Alison E.
Arena, Ellen T.
Eliceiri, Kevin W.
author_facet Rueden, Curtis T.
Schindelin, Johannes
Hiner, Mark C.
DeZonia, Barry E.
Walter, Alison E.
Arena, Ellen T.
Eliceiri, Kevin W.
author_sort Rueden, Curtis T.
collection PubMed
description BACKGROUND: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. RESULTS: We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS: Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-57080802017-12-06 ImageJ2: ImageJ for the next generation of scientific image data Rueden, Curtis T. Schindelin, Johannes Hiner, Mark C. DeZonia, Barry E. Walter, Alison E. Arena, Ellen T. Eliceiri, Kevin W. BMC Bioinformatics Software BACKGROUND: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. RESULTS: We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS: Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-29 /pmc/articles/PMC5708080/ /pubmed/29187165 http://dx.doi.org/10.1186/s12859-017-1934-z Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Rueden, Curtis T.
Schindelin, Johannes
Hiner, Mark C.
DeZonia, Barry E.
Walter, Alison E.
Arena, Ellen T.
Eliceiri, Kevin W.
ImageJ2: ImageJ for the next generation of scientific image data
title ImageJ2: ImageJ for the next generation of scientific image data
title_full ImageJ2: ImageJ for the next generation of scientific image data
title_fullStr ImageJ2: ImageJ for the next generation of scientific image data
title_full_unstemmed ImageJ2: ImageJ for the next generation of scientific image data
title_short ImageJ2: ImageJ for the next generation of scientific image data
title_sort imagej2: imagej for the next generation of scientific image data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5708080/
https://www.ncbi.nlm.nih.gov/pubmed/29187165
http://dx.doi.org/10.1186/s12859-017-1934-z
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