IM.Grid, a Grid computing approach for Image Mining of High Throughput-High Content Screening

Image processing and analysis has become essential for both cell biology research and drug discovery since the advent of high content screening (HCS) technologies. In this context, the Grid technology is a good opportunity to solve intensive computing problems with large data set. In addition, the exploitation of the Grid is not a simple task for many users because it is difficult to use the Grid in practical fields. Another important issue is to provide a simple way to use of Grid resources. In this paper, we present IM.Grid, a grid computing extension of our in-house image analysis software called IM (Image Mining) providing capabilities to simultaneously access visual data located on NAS (network-attached storage) and extract knowledge from the raw information by customizable image processing pipeline in a parallel way. A user makes a plug-in designing own image mining pipeline using specific built-in image processing libraries. Then, the plug-in becomes an actual processing unit when Grid starts to analyze multiple images retrieving them from the NAS at a time. The user receives output results as fast as numbers of computational grids are available. We apply this method to reduce the image processing and analysis time of cell biological images for drug discovery within high throughput-high content screening (HT-HCS) context. Because the processing time grows dramatically as the image size becomes huge due to many factors like multi-channel, high resolution and so on. To deal with these constraints, we propose a high-performance computing environment on .NET framework that helps to improve productivity not only in developing phases but also in HT-HCS platforms.