Document Detail

Classification of Bacterial Contamination Using Image Processing and Distributed Computing.
MedLine Citation:
PMID:  23060342     Owner:  NLM     Status:  Publisher    
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-vectormachine (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 largescale deployment of a light scatterbased approach to bacterial classification.
W Ahmed; B Bayraktar; A Bhunia; D Hirleman; J Robinson; B Rajwa
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-04
Journal Detail:
Title:  IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society     Volume:  -     ISSN:  1558-0032     ISO Abbreviation:  IEEE Trans Inf Technol Biomed     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9712259     Medline TA:  IEEE Trans Inf Technol Biomed     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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