Image-based classification for automating protein crystal identification

A technology for automatic evaluation of images from protein crystallization trials is presented in this paper. In order to minimize the interference posed by the environmental factors, the droplet is segmented from the entire image first. The algorithm selects different features, which are derived from the pixels within the droplet, and obtains a 16-dimensional feature vector which will then be fed to the classifier to make a classification. Each image is classified into one of the following classes: "Clear", "Precipitate" and "Crystal". We have achieved an accuracy rate of 84.8% with our algorithm.