Prof. Stephen On Profile

Prof. Stephen On

at Lincoln Univ

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Proceedings Article | 30 April 2019 Presentation + Paper

Classification of Arcobacter species using variational autoencoders

Valery Patsekin, Stephen On, Jennifer Sturgis, Euiwon Bae, Bartek Rajwa, Aleksandr Patsekin, J. Paul Robinson

Proceedings Volume 11016, 1101608 (2019) https://doi.org/10.1117/12.2521722

KEYWORDS: Feature extraction, Image classification, Statistical analysis, Databases, Data modeling, Biological research, Organisms, Machine learning, Image processing

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Arcobacter (formerly classified as Campylobacter spp.) are curved-to helical, Gram-negative, aerobic/microaerobic bacteria increasingly recognized as human and animal pathogens. In collaboration with Lincoln and Purdue University, we report the first experimental result of laser-based classification method of bacterial colonies of these species. This technology is based on elastic light scatter (ELS) phenomena where incident laser interacts with the whole volume of the colony and generates a unique fingerprint laser pattern. Here we report a novel development and application of deep learning algorithm to classify the scatter patterns of Arcobacter species using variational autoencoders (VAE). VAE creates set of normal distributions. Each of these distributions are responsible for certain properties of the original images. We used VAE to identify features in the features space for several hundred images which includes size of the colony based on scatter size, intensity of the image, and, the number of rings within the image, and so on. Thus each sample within our image database can be coded with sets of features that facilitates fast preliminary search for similar images allowing clustering of similar patterns in feature space. In addition, such initial selection could assist in identifying non-bacterial scatter patterns (i.e. bubbles or dust spots in the agar), or doublets where two colonies are overlapping during the acquisition time thus removing non-biological artifacts prior to analysis. An interesting result was that while VAE created far more realistic synthetic images closer to the original image, a simple autonencoder resulted in better cluster separation.

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