DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials
Vallotton, P., Sun, C., Lovell, D. R., Fazio, V. J., & Newman, J. (2010) DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials. Journal of Applied Crystallography, 43(6), pp. 1548-1552.
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. © 2010 International Union of Crystallography Printed in Singapore-all rights reserved.
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|Item Type:||Journal Article|
|Keywords:||automation, image analysis, mathematical morphology, protein crystallization, protein crystallography, shortest-path algorithms, Automated algorithms, High-throughput, Image analysis method, Manual inspection, New high, Processing time, Rate-limiting steps, Shortest path algorithms, Algorithms, Crystallization, Crystallography, Drops, Mineralogy, Morphology, Proteins|
|Subjects:||Australian and New Zealand Standard Research Classification > PHYSICAL SCIENCES (020000)
Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||International Union of Crystallography|
|Deposited On:||07 Jan 2015 03:50|
|Last Modified:||21 Jan 2015 04:55|
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