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'). [References: 18]
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