Paper
13 February 2012 Towards a colony counting system using hyperspectral imaging
B. Masschelein, A. Robles-Kelly, C. Blanch, N. Tack, B. Simpson-Young, A. Lambrechts
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Abstract
Colony counting is a procedure used in microbiology laboratories for food quality monitoring, environmental management, etc. Its purpose is to detect the level of contamination due to the presence and growth of bacteria, yeasts and molds in a given product. Current automated counters require a tedious training and setup procedure per product and bacteria type and do not cope well with diversity. This contrasts with the setting at microbiology laboratories, where a wide variety of food and bacteria types have to be screened on a daily basis. To overcome the limitations of current systems, we propose the use of hyperspectral imaging technology and examine the spectral variations induced by factors such as illumination, bacteria type, food source and age and type of the agar. To this end, we perform experiments making use of two alternative hyperspectral processing pipelines and compare our classification results to those yielded by color imagery. Our results show that colony counting may be automated through the automatic recovery of the illuminant power spectrum and reflectance. This is consistent with the notion that the recovery of the illuminant should minimize the variations in the spectra due to reflections, shadows and other photometric artifacts. We also illustrate how, with the reflectance at hand, the colonies can be counted making use of classical segmentation and classification algorithms.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B. Masschelein, A. Robles-Kelly, C. Blanch, N. Tack, B. Simpson-Young, and A. Lambrechts "Towards a colony counting system using hyperspectral imaging", Proc. SPIE 8225, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues X, 822510 (13 February 2012); https://doi.org/10.1117/12.908041
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Cited by 5 scholarly publications.
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KEYWORDS
Bacteria

Reflectivity

Principal component analysis

Microbiology

RGB color model

Hyperspectral imaging

Image processing

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