Knowledge Modeling for High Content Screening of Multimedia Biological Data

High-content and high-throughput screening (HCS/HTS) technologies provide powerful imaging tools for analyses of biological processes. These technologies combine sophisticated optics with automation techniques for imaging large populations of cells under different experimental perturbations and produce enormous amount of imaging data, including 2D images, 3D confocal data sets, time-lapse video sequences, and multispectral images. Manual analysis of such data is extremely time consuming, and intelligent image interpretation tools have only recently started to emerge. There is a direct need for powerful automated image understanding and spatio-temporal knowledge extraction techniques for gaining useful semantic information in biological domain consisting of multimodality multimedia data. In this tutorial we highlight key multimedia processing challenges in this domain and present a knowledge extraction and representation framework that is currently underway at Purdue University´s Cytometery Laboratories and Distributed Multimedia System Laboratory. The proposed framework is being implemented using XML in order to allow extensibility and standardization.