title: Classification of protein crystallization imagery.
abstract: We investigate automatic classification of protein crystallization imagery, and evaluate the performance of several modern mathematical tools when applied to the problem. For feature extraction, we try a combination of geometric and texture features; for classification algorithms, the support vector machine (SVM) is compared with an automatic decision-tree classifier. Experimental results from 520 images are presented for the binary classification problem: separating successful trials from failed attempts. The best false positive and false negative rates are at 14.6% and 9.6% respectively, achieved by feeding both sets of features to the decision-tree classifier with boosting.
authors.affiliation: Department of Electrical Engineering, Stanford University, CA, USA.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Zhu
authors.forename: Shaohua
authors.initials: S
authors.lastname: Sun
authors.forename: Marshall
authors.initials: M
authors.lastname: Bern