This paper presents a method for designing classifier to automate an evaluation process of protein crystallization growth states. The classifier is designed by binary decision tree and its classification sequence is decided in descending order of classification accuracy. Furthermore, for applying to automated crystallization system, some functions reducing mis-classification are appended to the classifier. The performance of this method has been evaluated by comparing classification result by using proposed method with the result by manual classification

In recent years, several projects have advanced the research and development related to the automation of the protein crystallization process. However, the evaluation of crystallization states has not been completely automated yet. In the usual crystallization process, the researchers evaluate the crystallization growth states of the protein solution samples based on one's visual impressions and assign them a score over and over again. Then it is required to make the work more efficient. The method presented here automates such evaluations. This method attempts to categorize the individual crystallization droplet images into five classes from A to E, based on their crystallization states. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process utilizing linear discriminant analysis. The performance of this method has been tested on our experiments using actual protein solution images.