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 ~93.5% validated on an image database which contained over 79,000 images.
Using genetic algorithms and neural networks REF , an accuracy of 93.5% average true performance (88% true positive and 99% true negative rates) is achieved for binary classification.
Leveraging genetic algorithm and neural network in automated protein crystal recognition
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Computer Science Medicine