Distributed ImageJ(Fiji): a framework for parallel image processing

Abstract

ImageJ is an open-source application widely used for image processing. It has developer API that can be used to implement new plugins for specific image processing tasks. However, ImageJ wasn't designed to work on distributed systems. Currently, it is still being used on single machines to process large medical images, which takes several hours to complete. In this article, we present the approaches to make several essential and widely used ImageJ plugins to work in a cluster. As the cluster nodes parallelly run the existing plugins for image processing, they write the results on a shared drive. But one of the main challenges is, merging those results with high accuracy. Several ImageJ plugins were developed to distribute tasks and generate combined results efficiently. The existing 3D-Object-Counter plugin was used for testing the designed system. The experimental results on the test images of 3D objects stored in Tagged Image File Format(TIFF) show faster processing time with high accuracy and similarity compared to the single machine-based results. The image processing time depends on the number of nodes in the cluster. So, we present a mathematical model that determines the cluster size automatically for optimizing the overall image processing time.