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ImageJ: A Powerful Image Processing Tool for Research in Life Sciences

ImageJ, developed by NIH, is a versatile image processing software for research institutions. It offers a wide range of functions, plugin support, and easy collaboration. Ideal for offline processing in various fields such as neurology, genetics, and material science.

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ImageJ: A Powerful Image Processing Tool for Research in Life Sciences

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  1. ImageJ Overview & Recommendations 13 January 2010 Michael D. Vaughan Imaging, Robotics, & Intelligent Systems Laboratory The University of Tennessee

  2. ImageJ • Developed by National Institute of Health since 1997 • Developed in Java • Windows, Linux, Mac, 32-bit, 64-bit

  3. Built-in Functions[4] • Opening/saving many image formats • Simple Image enhancement • Smoothing, sharpening, edge detection • Thresholding, brightness/contrast • Geometric operations • Crop, scale, resize, rotate, flip • Analysis • Measure area, mean, σ, min/max, histogram • Color processing • Split/merge channels, HSV/RGB, pseudocolor

  4. Plugins Available • Simple to add new plugins • Community-maintained repository • More than 500 plugins • Many research facilities develop their methods in ImageJ • User development • Macro recording • Plugin creation using Java

  5. What it is used for • “The main focus is to assist research in life sciences” [1] • “Originally intended for neuro-scientists, it accumulated enough functionality to attract scientists from a variety of fields, such as cell biology, parasitology, genetics, life sciences in general, material science, etc.” [2] • Makes 3D viewing and stack registration, segmentation, etc. much simpler • “User-written plugins make it possible to solve many image processing and analysis problems” [3]

  6. Pros/Cons • Very flexible • Handles many image types natively • Runs on any system that will run a JVM (v5+) • Can be run online in an applet (ImageJA) • Simple plugin development and sharing • Can code in Java or in several scripting languages or recordable macros • Mature software, 1st release in 1997 • Used by many research institutions • Accepted and respected • Easy to collaborate and share

  7. Pros/Cons • Java gives some overhead • Much has been done with the JVM to minimize this • “ImageJ is the fastest pure Java image processing program” [4] • ImageJ designed/used more for visualization and offline processing • Porting of existing methods would take some time • Incorporating ImageJ with the libraries from other equipment would be overly time-consuming or impossible

  8. Plugins tested • Hough Circles • Radius [10-20] • http://rsb.info.nih.gov/ij/plugins/hough-circles.html

  9. Plugins tested • Hough Circles • Radius [20-55]

  10. Plugins tested • Morphology

  11. Recommendations • ImageJ is a powerful tool, primarily geared to offline processing of biological data • For students with coding background in Java, it would be simple to develop or port algorithms for sharing with other universities • Having samples of the IRIS Lab’s work in an interactive applet would look impressive

  12. Alternatives • Fiji (Fiji Is Just ImageJ) • http://pacific.mpi-cbg.de/ • A distribution of ImageJ with Java, Java3D and many plugins bundled • Supports automatic updating of ImageJ and installed plugins • Geared toward developers • Allows bug submission • Version control • Supports several scripting languages

  13. Questions?

  14. References • [1] http://pacific.mpi-cbg.de/wiki/index.php/Fiji • [2] http://en.wikipedia.org/wiki/FIJI_(software) • [3] http://en.wikipedia.org/wiki/Imagej • [4] http://rsb.info.nih.gov/ij/features.html

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