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Automatic classification of protein crystallization images using a curve‐tracking algorithm
Author(s) -
Bern Marshall,
Goldberg David,
Stevens Raymond C.,
Kuhn Peter
Publication year - 2004
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s0021889804001761
Subject(s) - protein crystallization , classifier (uml) , computer science , artificial intelligence , false positive paradox , algorithm , crystallization , pattern recognition (psychology) , chemistry , organic chemistry
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').