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The purpose of this paper is to propose a state discrimination for crystallization samples (droplets), the purpose of which is to discriminate between diffractable extracts (crystal) and other objects.

The line feature from the image of the protein droplet was extracted and the state discriminated using a classifier based on line features. A support vector machine is used as the classifier.

In order to verify the performance of the proposed method, the growth state was discriminated experimentally using the images taken by TERA, an automated crystallization system. The correction ratio was determined to exceed 80 percent.

Contribution to automated evaluation process of the growth state of protein crystallization samples.

The purpose of this paper is to present classification schemes for the crystallization state of proteins utilizing image processing.

Two classification schemes shown here are combined sequentially.

The correct ratio of experimental result using the method presented here is approximately 70 per cent.

The paper is a contribution to automated evaluation crystal growth, combining two classifiers based on specific visual feature, sequentially.