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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ahmed, W.M. Bayraktar, B. Bhunia, A.K. Hirleman, E.D. Robinson, J.P. Rajwa, B. |
| Copyright Year | 2013 |
| Abstract | 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 nation's 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 this study, 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 Fisher's discriminant analysis, and subsequently a support-vector-machine 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. |
| Page Count | 8 |
| File Size | 928354 |
| Starting Page | 232 |
| Ending Page | 239 |
| File Format | |
| ISSN | 21682194 |
| Volume Number | 17 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Microorganisms Chebyshev approximation Polynomials Distributed computing Accuracy Lasers feature extraction Bacterial contamination classification distributed computing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Information Management Health Informatics Electrical and Electronic Engineering Computer Science Applications Biotechnology |
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