Automated vs. Manual Counting of Bacterial Colonies Using Hough Circle Transform: A Comparative Study for E. coli, S.aureus, and P.aeruginosa
- 1 Department of Pharmacy Practice, Bahauddin Zakariya University, Pakistan
- 2 Department of Computer Science, Kansas State University, United States
- 3 Department of Computer Science, National University of Modern Languages, Pakistan
- 4 Department of Computer Science and Engineering, University of Alaska Anchorage, United States
Abstract
Bacterial colony enumeration is an essential stage in microbiological research, allowing susceptibility to antibiotics assessment, monitoring of the environment, and clinical diagnostics. This research compares automated bacterial colony counting using the Hough Circle Transform to traditional manual counting methods. Objectives include evaluating counting accuracy and efficiency. This study adopted the image processing approach to colonies of E. coli, S. aureus, and P. aeruginosa. The proposed methodology achieved an overall accuracy of 95% for E. coli, 90% for S. aureus, and 84% for P. aeruginosa, with associated recall values of 95%, 91%, and 86%. The F-measure remained continuously high, ranging between 0.85 and 0.95. Regarding efficiency, manual counting required an average of 70 seconds per plate, while automated counting without and with visual correction took 30 seconds. The study contributes to improving laboratory efficiency, with implications for microbiological diagnostics. Future research is suggested to enhance preprocessing and segmentation techniques.
DOI: https://doi.org/10.3844/jcssp.2025.2312.2322
Copyright: © 2025 Areesha Rehman, Zikria Saleem, Jarrar Amjad, Syed Rehan Shah and Kamran Siddique. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 87 Views
- 17 Downloads
- 0 Citations
Download
Keywords
- Petri dishes
- Hough circle transform
- Automatic detection
- Image processing