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Proceedings Paper

Classification of Arcobacter species using variational autoencoders
Author(s): Valery Patsekin; Stephen On; Jennifer Sturgis; Euiwon Bae; Bartek Rajwa; Aleksandr Patsekin; J. Paul Robinson
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Paper Abstract

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.

Paper Details

Date Published: 30 April 2019
PDF: 8 pages
Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101608 (30 April 2019); doi: 10.1117/12.2521722
Show Author Affiliations
Valery Patsekin, Lincoln Univ. (New Zealand)
Stephen On, Lincoln Univ. (New Zealand)
Jennifer Sturgis, Lincoln Univ. (New Zealand)
Euiwon Bae, Purdue Polytechnic Institute (United States)
Bartek Rajwa, Purdue Polytechnic Institute (United States)
Aleksandr Patsekin, Purdue Polytechnic Institute (United States)
J. Paul Robinson, Lincoln Univ. (New Zealand)
Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 11016:
Sensing for Agriculture and Food Quality and Safety XI
Moon S. Kim; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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