Simplified method to automatically count bacterial colony forming unit (original) (raw)
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Image Processing Based Bacterial Colony Counter
Enumeration of Bacterial Colonies is required in many fields such as in clinical diagnosis, biomedical research for prevention of harmful diseases and pharmaceutical industry to avoid contamination of products. Existing Bacterial Colony counter systems count Bacterial Colony manually which is a time consuming, less efficient and tedious process. Hence, automation for counting of bacterial colony was required. The proposed method count these colonies automatically using image processing techniques. This method will provide a greater degree of accuracy in counting of bacterial colonies. Proposed technique takes an image of bacterial colony and converts it into grayscale. Otsu thresholding is applied for segmentation of the image further its conversion into binary image. After that, morphological operations are applied to clean up the image by removing noise and unnecessary pixels. Distance and watershed transformations are applied on the binary image to create partitions among overlapped and joint bacteria. Region properties and labeling information of segmented image is used for counting of bacterial colony.
An automated bacterial colony counting and classification system
Information Systems Frontiers, 2009
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.
PLOS ONE, 2020
Automated colony counting methods have long been known in Microbiology. Numerous methods for automated image analysis have been described and a wide range of commercial products exists. Known advantages are saving cost by reducing enumeration time, automatic documentation, reproducibility, and operator independence. Still, even today the realization of all advantages of automated image analysis makes it necessary to either invest in an expensive, high performance commercial system, or to acquire expert knowledge in image processing. This is a considerable obstacle for many laboratories, and the reason why manual colony counting is still done frequently. This article describes an easy to apply automatic colony counting system-including suggestions for sample preparation-that can be put into operation with basic knowledge of image processing and low budget.
Counting Bacteria Colonies Based on Image Processing Methods
2019
Counting of microbial colonies is crucial due to the applications of medical microbiology to search and detect the causes of diseases. While different tasks performed, the counting process of bacteria colonies is provided either by the searcher manually or by a common software, nowadays. The manual counting of bacteria colonies is tiresome, eye-straining, and time consuming for the searcher where common softwares require high troublesome with having high error rates. The aim of this study is detecting and counting bacteria colonies without having these limitations in today's non-practical applications. Therefore, an image-processing based bacteria colony counter designed in MATLAB. In the medical plasma laboratory of the Izmir Katip Celebi University three different types of hospital-acquired infection cause bacterias, which are Escherichia coli, Pseudomonas aeruginosa, and Enterococcus faecalis, cultured and examined properly, then, using the Circular Hough Transform (CHT) in MATLAB the detection and counting of bacteria colonies provided. To be able to obtain more practical usage, a Graphical User Interface (GUI) designed.
Applied and environmental microbiology, 1998
In this work we introduce the confluent and various sizes image analysis method (COVASIAM), an automated colony count technique that uses digital imaging technology for detection and separation of confluent microbial colonies and colonies of various sizes growing on petri dishes. The proposed method takes advantage of the optical properties of the surfaces of most microbial colonies. Colonies in the petri dish are epi-illuminated in order to direct the reflection of concentrated light coming from a halogen lamp towards an image-sensing device. In conjunction, a multilevel threshold algorithm is proposed for colony separation and counting. These procedures improved the quantification of colonies showing confluence or differences in size. We tested COVASIAM with a sample set of microorganisms that form colonies with contrasting physical properties: Saccharomyces cerevisiae, Aspergillus nidulans, Escherichia coli, Azotobacter vinelandii, Pseudomonas aeruginosa, and Rhizobium etli. Thes...
Method for Counting Microorganisms and Colonies in Microscopic Images
12th International Conference on Computational Science and Its Applications (ICCSA), 2012
This paper presents a method to count microorganisms and colonies in microscopic images. The method uses a series of morphological operations to create a representation in which the objects of interest are easily isolated and counted. The proposal is successful in most cases, properly dealing with some difficult situations like when the sizes of the objects vary strongly and when there is low contrast between the objects and the background. Studies are underway in order to improve the performance of the method when dealing with strongly merged objects.
Semi-automatic model to colony forming units counting
International Journal of Electrical and Computer Engineering (IJECE), 2023
Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semiautomatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar.
An Automated System for Rapid Non-Destructive Enumeration of Growing Microbes
PLOS One, 2010
Background: The power and simplicity of visual colony counting have made it the mainstay of microbiological analysis for more than 130 years. A disadvantage of the method is the long time required to generate visible colonies from cells in a sample. New rapid testing technologies generally have failed to maintain one or more of the major advantages of culture-based methods.
Micro-colony observation: A rapid and simple approach to count bacterial colony forming units
2022
ABSTRACTFor enumerating viable bacteria, traditional dilution plating to count colony forming units (CFU) has always been the preferred method in microbiology owing to its simplicity, albeit laborious and time-consuming. Similar CFU counts can be obtained by quantifying growing microcolonies in conjunction with the perks of a microscope. Here, we employed a simple method of five microliter spotting of differently diluted bacterial culture multiples times on a single petri plate followed by finding out CFU by counting microcolonies using phase contrast microscope. In this method within four-hour period CFU of an Escherichia coli culture can be found out. Further, within ten-hour period, CFU in a culture of Ralstonia solanacearum, a bacterium with generation time around 3 h, can be estimated. The CFU number determined by microcolonies observed is comparable with that obtained by the dilution plating method. Microcolonies number observed in the early hours of growth (2 h in case of E. ...
Letters in Applied Microbiology, 2018
Human Visual vs. Automated Colony Counting SIGNIFICANCE AND IMPACT OF THE STUDY: Colony quantification is essential in clinical and research settings as well as pedagogy at the college level. Human visual counting (HV), the most common method, is time consuming and fraught with errors. The time, accuracy and precision of HV counting was compared to a high end professional automated counter, an inexpensive phone application (app), and a free phone app. Low cost benefits of increased speed and accuracy with automated counting is maximized when counting single round colonies; but much reduced if colonies have irregular morphology or demonstrate hemolysis. ABSTRACT: To evaluate comparative efficiency of traditional vs. automated colony counting methods, cultures of Escherichia coli (ATCC 25945), Staphylococcus epidermidis (ATCC 12225), Streptococcus pyogenes (ATCC19615), and Streptococcus pneumoniae (ATCC49619) were prepared as pure cultures and mixed cultures at 0.5 McFarland standard and serial dilutions were performed. Plates were inoculated in triplicate with 50 CFUs, 125 CFUs, 250 CFUs and 500 CFUs and counted by four researchers, visually and using each of the automated counters. Colony count and counting time were recorded. The pattern of efficiency for all bacterial species was similar: plates with low counts were accurate and quick to count for all methods, with an increase in time and a decrease in accuracy and precision as counts rose. Higher counts of single round colonies required less time and had greater precision with automated counters than HV counts with no loss of accuracy, however, counts were reduced in accuracy and increased in time for species with less regular morphology or when plates had mixed species. Surprisingly, a free phone application was only slightly less precise and more time consuming than the high end professional counter indicating that automation may be achievable at lower cost than expected.