#NIH IMAGEJ SOFTWARE SOFTWARE#

Thus, life scientists needed accessible methods to execute image-processing and analysis techniques to quantitatively support their research.ДЄ large number of reviews have been written on the subject of biomedical image processing and analysis tools in general (e.g., Eliceiri et al. Advances in biomedical image-processing techniques have allowed for the corroboration of research outcomes by quantifying them in a rigid, statistical manner while simultaneously raising the bar for life science research when it comes to substantiating scientific observations. Compared to conveniently uniform footage (e.g., from a video camera) biomedical images require substantial knowledge about the physical intricacies of the optics involved, coupled with textbook computer vision expertise, for sound image processing. As the field of image processing matured (e.g., Castleman 1996), computer-vision experts developed specialized techniques that could be applied to biomedical images.Д«iomedical image processing is a subset of computer-vision research with its own specific challenges вЂ“ namely, low-light conditions required to keep the imaged specimen alive. The aim is to use computational processes to accelerate repetitive tasks while also obtaining quantitative results, since statistical results are much more compelling, scientifically speaking, than qualitative observations.

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.Д®ver 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.

#NIH IMAGEJ SOFTWARE SOFTWARE#

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 вЂ“ from commercial to academic, special-purpose to Swiss army knife, small to largeвЂ“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.

Nih imagej software