Research Project: Digital Image Analysis

Project Description

When presented with large numbers of samples to analyze, the traditional methods of manual counting can present a daunting challenge to even the most seasoned researcher. Given the advances in speed of most desktop processors, real-time pixel by pixel operations on hi-resolution images now falls within the realm of feasibility. Our goal is the development of hi-end image processing tools that allow for automated, as well as manual analysis techniques to be applied to large sequences of images in the least amount of time possible. To this end, we are exploring the use of various background correction, noise reduction, spatial filtering, masking, and object auto-detection algorithms.

Our most recent project is the development of a Java based image analysis system, Skipper. Skipper combines all of our previous tools and research development into one complete system which is comprised of several different components. Skipper includes autodetection algorithms for the automatic processing of acquired images, various measurement and object grouping tools, report generation system, and also it has a build in support for image acquisition. The open-architecture of Skipper allows for quick and easy development of additional plug-ins and add-ons. In the future we are planning to add support for ImageJ, so that people using Skipper will be able to benefit from the already existing ImageJ plug-ins.

Skipper is a comprehensive Java-based image processing package that was primarily developed for scientific research, allowing researchers to automatically categorize, measure, and enumerate tiny objects such as bacteria and other aquatic organisms. The software has multiple algorithms implemented for the automatic enumeration of bacteria, plankton, and other organisms, which makes the processing of multiple images fast and removes the aspect of human bias in counting. They work equally well for images (photographs) derived from astronomy, biology, geology, physics, and other fields of research. Various image processing functions, such as Gaussian blur, contrast manipulation, spatial convolutions with userdefined convolution masks, erosion, sharpening, mean and median filtering, edge detection, and others are also included and can be used to enhance the image quality. The program allows researchers to organize their images into a single project and produce printable reports that contain useful statistics about the obtained data. Multiple images can be overlaid and the data compared automatically, ensuring accurate tagging of cells that fluoresce. Skipper has an open architecture that allows researchers to add their own enumeration and image filtering functions as plug-ins.