We specifically explore what makes ImageJ so popular, how it impacts life science, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem.Įver since digital-imaging equipment entered the world of science, life scientists have collaborated with computer scientists to apply image-processing techniques to analyze biomedical data. 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. 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. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. 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. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. 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 life sciences, and continues to do so. 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. 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.
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