The use of gradient direction in pre-processing images from crystallization experiments

Abstract

Robots are now used routinely to perform crystallization experiments and many laboratories now have imaging systems to record the results. These images must be evaluated rapidly and the results fed back into optimization procedures. Software to analyse the images is being developed; described here are methods to restrict the area of the image to be analysed in order to speed up processing. Properties of the gradient of greyscale images are used to identify first the well and then the crystallization drop for various crystallization trays and different imaging systems. Methods are discussed to identify artefacts in the images that are not related to the experimental outcome, but can cause problems for the machine-learning algorithms used in classification and waste time during analysis. Gradient angles are exploited to eliminate faults in the crystallization trays, bubbles and splatter droplets prior to analysis.