7- https://doi.org/10.1107/s090744490602614x
Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers
- Aug 19, 2006
- Acta Crystallographica Section D Biological Crystallography
- Kuniaki Kawabata +6 more
In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%.
- # Nonlinear Classifiers
- # Visual Impressions
- # Linear Discriminant Analysis
- # Categorization Process
- # Linear Classifiers
- # Concordance Rate
- # Support Vector Machine
- # Multiple Classifiers
- # Human Expert
- # Development Of Robotic Systems
