Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Hogekamp, Lola; Hogekamp, Stefan H.; Stahl, Mario

doi:10.1371/journal.pone.0232869

Cited by 15 publications

(9 citation statements)

References 13 publications

(15 reference statements)

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“…Previous studies on the counting of bacteria colonies have varied in both the type of bacteria counted and the methods employed. Hogekamp et al (2020) developed an automated image-based system for counting Escherichia coli DH5-Alpha colonies. When each petri dish image was imported into Hogekamp's program, it was divided into 6 equal parts similar to slicing a pizza.…”

Section: Related Workmentioning

confidence: 99%

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Image-based Automatic Counting of Bacillus cereus Colonies using Smartphone

Taithong¹,

Wichai²,

Waranusast³

et al. 2022

IJACSA

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Substantial amounts of Bacillus cereus bacteria present in food indicates that the food is unsafe to eat, so counting B. cereus colonies in food samples is a common test for food cleanliness. Manual counting of B. cereus bacteria colonies requires approximately 2-5 minutes per Petri dish, depending on the number of colonies present. This study presents a new smartphone-based method called Bacillus Cereus Image Counting System (BCICS, "B. kiks") for automatic counting of B. cereus colonies. BCICS uses image processing techniques including Projection Profiling, Circle Hough Transformation, Adaptive Thresholding, and Power-Law Transformation to achieve high image clarity and then uses the Connected-Component Labeling (CCL) technique to correctly count the colonies, including overlapping colonies. These techniques are built into a convenient Android smartphone application. Resultsof counting the colonies with BCICS were compared with results of hand counting the same dishes. The accuracy rate of each dish count was calculated, as well as the average dish accuracy across all dishes. BCICS counted total colonies with an accuracy of 90.14%, which is close to that of hand counting accuracy since hand counting itself commonly involves an error rate of 5-10%. Importantly, the application took only 3-5 seconds to count one Petri dish, which is more than 74 times faster than the time required for manual counting.

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Section: Related Workmentioning

confidence: 99%

“…The resulting number of colonies was multiplied by 6 to make up for the unprocessed image parts. The main problem that caused counting errors was the random selection of image parts, because the parts did not all have similar numbers of colonies [10].…”

Section: Related Workmentioning

confidence: 99%

Image-based Automatic Counting of Bacillus cereus Colonies using Smartphone

Taithong¹,

Wichai²,

Waranusast³

et al. 2022

IJACSA

View full text Add to dashboard Cite

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“…Previous researchers have recorded digital images of Petri dish using webcams, DSLR cameras, smartphones, and flatbed scanners [7]. For example, a recent study used a Canon EOS 500D 15 MP SLR Camera to record digital plate images in grayscale and RGB color at 300 and 800 dpi resolution, respectively [11]. Another recent study employed the Canon EOS 1200D reflex camera triggered by an Arduino controller to record plate images [2].…”

Section: Hardware In Contextmentioning

confidence: 99%

“…Another recent study employed the Canon EOS 1200D reflex camera triggered by an Arduino controller to record plate images [2]. In addition, a number of open source software have been released for this application [7,[9][10][11][12][13]. Over the years, image processing and analysis techniques have significantly refined to improve robustness, versatility, and user-friendliness.…”

Section: Hardware In Contextmentioning

confidence: 99%

Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times

et al. 2021

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“…In addition, it is not immediate, requiring the incubation period for colony growth. Automated bacterial counters are also available as fully automated counting systems but can be highly expensive and not affordable for small labs, or multiple instruments may be required for large facilities and research centers [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

2022

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Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.

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Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Cited by 15 publications

References 13 publications

Image-based Automatic Counting of Bacillus cereus Colonies using Smartphone

Image-based Automatic Counting of Bacillus cereus Colonies using Smartphone

Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

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