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An automated bacterial colony counting and classification system

Author

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  • Wei-Bang Chen

    (University of Alabama at Birmingham)

  • Chengcui Zhang

    (University of Alabama at Birmingham)

Abstract

Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there may exist hundreds or thousands of colonies on a Petri dish, and the counting process is usually manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter which can not only count but also classify colonies. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, the proposed method is software-centered and can accept general digital camera images as its input. The counting process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and reporting colony counts. In order to differentiate colonies of different species, the proposed counter adopts one-class Support Vector Machine (SVM) with Radial Basis Function (RBF) as the classifier. Our proposed counter demonstrates a promising performance in terms of both precision and recall, and is robust and efficient in terms of labor-and time-savings.

Suggested Citation

  • Wei-Bang Chen & Chengcui Zhang, 2009. "An automated bacterial colony counting and classification system," Information Systems Frontiers, Springer, vol. 11(4), pages 349-368, September.
    • Handle: RePEc:spr:infosf:v:11:y:2009:i:4:d:10.1007_s10796-009-9149-0
    DOI: 10.1007/s10796-009-9149-0
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    1. Opher Etzion & Amit Fisher & Segev Wasserkrug, 2005. "e-CLV: A Modeling Approach for Customer Lifetime Evaluation in e-Commerce Domains, with an Application and Case Study for Online Auction," Information Systems Frontiers, Springer, vol. 7(4), pages 421-434, December.
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    Cited by:

    1. Muthu Subash Kavitha & Takio Kurita & Soon-Yong Park & Sung-Il Chien & Jae-Sung Bae & Byeong-Cheol Ahn, 2017. "Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.

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