Mark Hiner

The ImageJ ecosystem: An open platform for biomedical image analysis

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 softwares 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. Mol. Reprod. Dev. 82: 518–529, 2015.

ImageJ2: ImageJ for the next generation of scientific image data

BackgroundImageJ 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.ResultsWe 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.ConclusionsScientific 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.

Quantitating the cell: turning images into numbers with ImageJ

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

SCIFIO: an extensible framework to support scientific image formats

BackgroundNo gold standard exists in the world of scientific image acquisition; a proliferation of instruments each with its own proprietary data format has made out-of-the-box sharing of that data nearly impossible. In the field of light microscopy, the Bio-Formats library was designed to translate such proprietary data formats to a common, open-source schema, enabling sharing and reproduction of scientific results. While Bio-Formats has proved successful for microscopy images, the greater scientific community was lacking a domain-independent framework for format translation.ResultsSCIFIO (SCientific Image Format Input and Output) is presented as a freely available, open-source library unifying the mechanisms of reading and writing image data. The core of SCIFIO is its modular definition of formats, the design of which clearly outlines the components of image I/O to encourage extensibility, facilitated by the dynamic discovery of the SciJava plugin framework. SCIFIO is structured to support coexistence of multiple domain-specific open exchange formats, such as Bio-Formats’ OME-TIFF, within a unified environment.ConclusionsSCIFIO is a freely available software library developed to standardize the process of reading and writing scientific image formats.

ImageJ-MATLAB: a bidirectional framework for scientific image analysis interoperability.

Summary: ImageJ‐MATLAB is a lightweight Java library facilitating bi‐directional interoperability between MATLAB and ImageJ. 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. Availability and Implementation: Freely available extension to ImageJ2 (http://imagej.net/Downloads). Installation and use instructions available at http://imagej.net/MATLAB_Scripting. Tested with ImageJ 2.0.0‐rc‐54, Java 1.8.0_66 and MATLAB R2015b. Contact: eliceiri@wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

ImageJ: Image Analysis Interoperability for the Next Generation of Biological Image Data

Biological imaging has greatly advanced over the last thirty years with the now unprecedented ability to track biological phenomena in high resolution in physiologically relevant conditions over time and in space. As these imaging technologies mature and become main stream tools for the bench biologist there is great need for improved software tools that drive the informatics workflow of the imaging process from acquisition and image analysis to visualization and dissemination. To best meet the workflow challenges, these tools need to be freely available, open source, and transparent in their development and deployment. In particular it is clear that given the complexity, and heterogeneity of the modern image dataset, there can not be a single software solution. Different imaging processing and visualization approaches need access not only to the data but 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 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] family of image analysis, FIJI [2] and ImageJ2 [3].

Open Source BioImage Informatics: Tools for Interoperability

Biological imaging has greatly advanced over the last thirty years with the now unprecedented ability to track biological phenomena in high resolution in physiologically relevant conditions over time and in space. As these imaging technologies mature and become main stream tools for the bench biologist there is great need for improved software tools that drive the informatics workflow of the imaging process from acquisition and image analysis to visualization and dissemination. To best meet the workflow challenges, these tools need to be freely available, open source, and transparent in their development and deployment. In particular it is clear that given the complexity, and heterogeneity of the modern image dataset, there can not be a single software solution. Different imaging processing and visualization approaches need access not only to the data but 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 in the FIJI and Open Microscopy Environment consortiums. The consortiums are actively developing key software libraries like ImgLib and Bio-Formats that are utilized in dozens of software applications to parse and visualize biological image data, to the developmental benefit of not only of the applications but the libraries themselves. Biography Kevin Eliceiri received his undergraduate and graduate training in Microbiology and Biotechnology at the University of Wisconsin in Madison. He worked in the R.M. Bock laboratory developing imaging approaches for the model nematode C. elegans. He received further post-graduate training at the National Integrated Microscopy Resource (Madison, Wisconsin) in the area of computer science and microscopy. Since 2000 he has been at the Laboratory for Optical and Computational Instrumentation (LOCI) at the University of Wisconsin at Madison. He is currently director of the LOCI and a Principal Investigator in the Laboratory of Molecular Biology at the University of Wisconsin in Madison Graduate School. He holds research investigator appointments in the Departments of Biomedical Engineering and Medical Physics and is a full investigator in the University of Wisconsin Comprehensive Cancer Center. His current research focuses on the development of novel optical imaging methods for investigating signaling and cell interaction in development and disease processes, and the development of software for multidimensional image informatics.