title: Leveraging genetic algorithm and neural network in automated protein crystal recognition.
abstract: 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 approximately 93.5% validated on an image database which contained over 79,000 images.
authors.affiliation: Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. ts2060@Columbia.edu
authors.forename: Ming Jack
authors.initials: MJ
authors.lastname: Po
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Laine
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D003460:Crystallization; D005104:Expert Testimony; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D012985:Software Design
chemical_list: D011506:Proteins