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Feature analysis for classification of trace fluorescent labeled protein crystallization images

Authors
  • Sigdel, Madhav1
  • Dinc, Imren2
  • Sigdel, Madhu S.1
  • Dinc, Semih1
  • Pusey, Marc L.3
  • Aygun, Ramazan S.1
  • 1 University of Alabama in Huntsville, Computer Science Department, Huntsville, Alabama, 35899, USA , Huntsville (United States)
  • 2 Troy University, Computer Science Department, Troy, Alabama, 36082, USA , Troy (United States)
  • 3 iXpressGenes, Inc., 601 Genome Way, Huntsville, Alabama, 35806, USA , Huntsville (United States)
Type
Published Article
Journal
BioData Mining
Publisher
BioMed Central
Publication Date
Apr 27, 2017
Volume
10
Issue
1
Identifiers
DOI: 10.1186/s13040-017-0133-9
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundLarge number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated.ResultsFeatures are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu’s, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu’s thresholding, region features using green percentile G90 thresholding, region features using green percentile G99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier.ContributionsThe feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis.

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