Leveraging Genetic Algorithm and Neural Network in Automated Protein Crystal Recognition

Po, Ming J.; Laine, Andrew F.

We propose a classification framework combined with a multi-scale image processing method for recognizing protein crystals in high-throughput images. The main three points of the processing method are the multiple population genetic algorithm for region of interest detection, multi-scale Laplacian pyramid filters and histogram analysis techniques to find an effective feature vector. Using human (expert crystallographers) classified images as ground truth, the current experimental results gave 88% true positive and 99% true negative rates, resulting in an average true performance of ~93.5% validated on an image database which contained over 79,000 images.


Also Published In

EMBC 2008: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: "Personalized Healthcare Through Technology": August 20-24, 2008, Vancouver, British Columbia, Canada

More About This Work

Academic Units
Biomedical Engineering
Published Here
August 12, 2010