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The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization campaigns automatically.

Supporting information

zip

Zip compressed file https://doi.org/10.1107/S0021889810040963/cg5163sup1.zip
Image files for test data set

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Zip compressed file https://doi.org/10.1107/S0021889810040963/cg5163sup2.zip
DroplIT program code

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Joint Photographic Experts Group (JPEG) image https://doi.org/10.1107/S0021889810040963/cg5163sup3.jpg
Original droplet image for testing the software


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