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ImageJ2: ImageJ for the next generation of scientific image data

BMC Bioinformatics · 2017 · Vol. 18(1) · pp. 529–529

Curtis Rueden✉(University of Wisconsin–Madison)Johannes Schindelin(University of Wisconsin–Madison)Mark Hiner(University of Wisconsin–Madison)Barry E. DeZonia(University of Wisconsin–Madison)Alison E. Walter(University of Wisconsin–Madison)Ellen T. Arena(University of Wisconsin–Madison)Kevin W. Eliceiri(University of Wisconsin–Madison)

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Abstract

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.

Cell Image Analysis Techniques Image Processing Techniques and Applications AI in cancer detectionComputer scienceData scienceImage (mathematics)Computer graphics (images)Information retrievalArtificial intelligenceComputer vision