Classification of bacterial contamination using image processing and distributed computing.
Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In the presented work we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine (SVM) classifier with a linear kernel was used. With extensive testing we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.
W M
WM
Ahmed
B
B
Bayraktar
A
A
Bhunia
E D
ED
Hirleman
J P
JP
Robinson
B
B
Rajwa
D001419:Bacteria / Q000737:chemistry* / Q000145:classification* / Q000302:isolation & purification; D015373:Bacterial Typing Techniques / Q000379:methods*; D016002:Discriminant Analysis; D005516:Food Microbiology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D012542:Scattering, Radiation; D060388:Support Vector Machine; D014733:Vibrio / Q000737:chemistry / Q000145:classification / Q000302:isolation & purification