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Research Papers

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

[+] Author Affiliations
Bulent Bayraktar

Purdue University, Bindley Bioscience Center, Purdue University Cytometry Laboratories, Department of Electrical and Computer Engineering, West Lafayette, Indiana 47907

Padmapriya P. Banada

Purdue University, Molecular Food Microbiology Laboratory, Lafayette, Indiana 47907

E. Daniel Hirleman

Purdue University, School of Mechanical Engineering, Lafayette, Indiana 47907

Arun K. Bhunia

Purdue University, Molecular Food Microbiology Laboratory, Lafayette, Indiana 47907

J. Paul Robinson

Purdue University, Bindley Bioscience Center, Purdue University Cytometry Laboratories, Department of Basic Medical Sciences, School of Veterinary Medicine and Weldon School of Biomedical Engineering, West Lafayette, Indiana 47907

Bartek Rajwa

Purdue University, Bindley Bioscience Center, Purdue University Cytometry Laboratories, Department of Basic Medical Sciences, West Lafayette, Indiana 47907

J. Biomed. Opt. 11(3), 034006 (May 19, 2006). doi:10.1117/1.2203987
History: Received November 10, 2005; Revised January 12, 2006; Accepted February 23, 2006; Published May 19, 2006
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Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.

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© 2006 Society of Photo-Optical Instrumentation Engineers

Citation

Bulent Bayraktar ; Padmapriya P. Banada ; E. Daniel Hirleman ; Arun K. Bhunia ; J. Paul Robinson, et al.
"Feature extraction from light-scatter patterns of Listeria colonies for identification and classification", J. Biomed. Opt. 11(3), 034006 (May 19, 2006). ; http://dx.doi.org/10.1117/1.2203987


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