| In recent years, high throughput robotic set-ups have been developed to automate the protein crystallization experiments, and imaging techniques are used to identify the state change or possibility of forming crystals. This dissertation proposes a framework for real-time analysis of protein crystallization trial images. Firstly, it provides a reliable and efficient classification of crystallization trials according to crystallization outcomes on a stand-alone system. Identification of the crystallization outcome of a trial is a multi-class classification problem where categories are ranked. Secondly, the framework provides spatio-temporal analysis of protein crystal growth by analyzing the time series images of a protein crystallization trial. In this dissertation, we propose techniques for a) feature extraction from protein crystallization trial images for classification, b) two-level classification: classification into high-level categories (non-crystals, likely-leads, and crystals) and sub-classification of crystal types, c) spatio-temporal analysis of protein crystallization trial images, and d) new accuracy measure to evaluate the performance of classification results. Overall, we propose an efficient and reliable framework for analysis of protein crystallization trials. |
