scispace - formally typeset

Journal ArticleDOI

NIH Image to ImageJ: 25 years of image analysis

01 Jul 2012-Nature Methods (Nature Research)-Vol. 9, Iss: 7, pp 671-675

TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
Topics: Image processing (54%), Bioimage informatics (52%)
Citations
More filters


Journal ArticleDOI
29 Nov 2017-BMC Bioinformatics
Abstract: 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. 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. 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.

2,925 citations


Journal ArticleDOI
19 Jun 2014-PeerJ
TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

2,367 citations


Cites background from "NIH Image to ImageJ: 25 years of im..."

  • ...ImageJ and its batteries-included Fiji distribution are probably the most popular open-source tools for image analysis (Schneider et al., 2012; Schindelin et al., 2012)....

    [...]


Posted Content
TL;DR: The entire ImageJ codebase was rewrote, 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.
Abstract: 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. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering 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. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

2,156 citations


Cites methods from "NIH Image to ImageJ: 25 years of im..."

  • ...Background ImageJ [1] is a powerful, oft-referenced platform for image processing, developed by Wayne Rasband at the National Institutes of Health (NIH)....

    [...]


Journal ArticleDOI
TL;DR: The ImageJ project is used as a case study of how open‐source software fosters its suites of software tools, making multitudes of image‐analysis technology easily accessible to the scientific community.
Abstract: Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available-from commercial to academic, special-purpose to Swiss army knife, small to large-but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem.

1,724 citations


Cites background from "NIH Image to ImageJ: 25 years of im..."

  • ...Rather than paraphrasing these excellent resources, this review will focus on the ImageJ project (Schneider et al., 2012) as a case study of how open-source software fosters an ecosystem of software tools, making an abundance of image-analysis methods and approaches easily accessible to the…...

    [...]

  • ...lent resources, this review will focus on the ImageJ project (Schneider et al., 2012) as a case study of how open-source software fosters an ecosystem of software tools, making an abundance of image-analysis methods and approaches easily accessible to the scientific community....

    [...]


References
More filters

Journal ArticleDOI
01 Jul 2012-Nature Methods
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

30,888 citations


Journal ArticleDOI
31 Oct 2006-Genome Biology
TL;DR: The first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler is described, which can address a variety of biological questions quantitatively.
Abstract: Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

3,916 citations


Journal ArticleDOI
Tony J. Collins1Institutions (1)
01 Jul 2007-BioTechniques
TL;DR: The near-comprehensive range of import filters that allow easy access to image and meta-data, a broad suite processing and analysis routine, and enthusiastic support from a friendly mailing list are invaluable for all microscopy labs and facilities-not just those on a budget.
Abstract: of short add-on programs to provide additional functionality to the core program. These additional files are either written in Java (the plugins) or in ImageJ’s macro programming language (macros). Once saved to the ImageJ plugins folder, these functions are loaded on start-up and can be accessed via menu commands like any other core function. 400+ PLUGINS

1,526 citations


Journal ArticleDOI
Arthur D. Edelstein1, Nenad Amodaj, Karl Hoover1, Ronald D. Vale2  +3 moreInstitutions (2)
TL;DR: This unit provides step‐by‐step protocols describing how to get started working with µManager, as well as some starting points for advanced use of the software.
Abstract: With the advent of digital cameras and motorization of mechanical components, computer control of microscopes has become increasingly important. Software for microscope image acquisition should not only be easy to use, but also enable and encourage novel approaches. The open-source software package µManager aims to fulfill those goals. This unit provides step-by-step protocols describing how to get started working with µManager, as well as some starting points for advanced use of the software.

1,364 citations


Journal ArticleDOI
01 Jul 2012-Nature Methods
TL;DR: Icy is a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows.
Abstract: Icy is a collaborative platform for biological image analysis that extends reproducible research principles by facilitating and stimulating the contribution and sharing of algorithm-based tools and protocols between researchers. Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/ .

1,018 citations


Network Information
Related Papers (5)
Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
2022117
20216,109
20205,858
20195,019
20184,324
20173,937