Automatic classification of protein crystallization images using a curve-tracking algorithm

Abstract

An algorithm for automatic classification of protein crystallization images acquired from a high-throughput vapor-diffusion system is described. The classifier uses edge detection followed by dynamic-programming curve tracking to determine the drop boundary; this technique optimizes a scoring function that incorporates roundness, smoothness and gradient intensity. The classifier focuses on the most promising region in the drop and computes a number of statistical features, including some derived from the Hough transform and from curve tracking. The five classes of images are `Empty', `Clear', `Precipitate', `Microcrystal Hit' and `Crystal'. On test data, the classifier gives about 12% false negatives (true crystals called `Empty', `Clear' or `Precipitate') and about 14% false positives (true clears or precipitates called `Crystal' or `Microcrystal Hit').