Simplified method to automatically count bacterial colony forming unit (original) (raw)
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.
Bacteria contaminated water is one of the reasons of water pollution which causes different diseases. This single celled microscopic organisms which thrive in diverse environment & stay in colonies. Counting those colonies it can be said the amount of bacteria available on a specific sample. To grow these colonies different culture & filtration procedures are used. After the growth colonies are usually being counted on naked eye. This paper represents an automated counting system the number of colonies which are present in a sample using image processing techniques. Colonies are considered as circular objects or discs and clustered as a colony with respect to its shape & ratio. Complexity of water quality is growing higher day by day. General criterion used to measure water quality relate to the security of human contact. Most of those criterions involves a lot amount time and money and also have some availability issues. In this paper there is described a new way of measuring water quality completely based on the image processing techniques which assures simplicity & affordability everywhere. Just one 250X+ zoom lens is all you need which could be attached to the phone which compares particles of water pixel by pixel & search for the impure particle of a water image. This procedure can be used as pre laboratory test for its costeffective design, field-portability this sensitive and specific contamination measurement procedure running on mobile could be rather useful as pre-laboratory water test at anyplace anywhere.
Nondestructive technique for bacterial count based on image processing
Biology, Engineering and Medicine, 2016
Microorganism plate count method is widely used in food and medicine industry, and is often used to determine the survival and proliferation of bacteria. The number of colonies in a culture is counted to calculate the concentration of bacteria in the original broth under specific conditions; however, manual counting can be time consuming and imprecise, derived from the human eyestrain. To contribute to the improvement of laboratory test methodologies, a cylindrical shape prototype was developed for automatic counting of bacterial colonies by applying digital image processing. The images of the colonies cultivated in petri dishes (PD) were recorded through a commercial CCD, and then processed with Scilab open source software. The results showed a linear relation between the manual and automatic method with a χ 2 =0.987 and a correlation coefficient of 0.994. The effectiveness of the proposed system was compared with verified manual counts at different light intensities, as well as to the use of two open source softwares. Results showed better reliability in counting these bacterial strains compared with visual counting. The main benefit of using a device with these features is to obtain counts in the shortest time possible. Moreover, the variation of the counts shows that the automated systems are more consistent than the manual counts.
Sensors & Transducers, 2016
This paper presents an arrangement based on a dedicated computer and charge-coupled device (CCD) sensor system to intelligently allow the counting and recognition of colony formation. Microbes in agricultural environments are important catalysts of global carbon and nitrogen cycles, including the production and consumption of greenhouse gases in soil. Some microbes produce greenhouse gases such as carbon dioxide and nitrous oxide while decomposing organic matter in soil. Others consume methane from the atmosphere, helping to mitigate climate change. The magnitude of each of these processes is influenced by human activities and impacts the warming potential of Earth's atmosphere. In this context, bacterial colony counting is important and requires sophisticated analysis methods. The method implemented in this study uses digital image processing techniques, including the Hough Transform for circular objects. The visual environment Borland Builder C++ was used for development, and ...
Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing
Cytometry, 2004
BackgroundClonogenic assays are used frequently to measure the cell killing and mutagenic effects of radiation and other agents. Clonogenic assays carried out manually are tedious and time-consuming and involve a significant element of subjectivity. However, several commercial automatic colony counters are available. Based on CCD video imaging and image analysis they are relatively expensive and can analyze only one petri dish at a time.Clonogenic assays are used frequently to measure the cell killing and mutagenic effects of radiation and other agents. Clonogenic assays carried out manually are tedious and time-consuming and involve a significant element of subjectivity. However, several commercial automatic colony counters are available. Based on CCD video imaging and image analysis they are relatively expensive and can analyze only one petri dish at a time.MethodWe have developed a cheaper and more efficient device, which employs a flat bed scanner to image 12 60-mm petri dishes at a time. Two major problems in automated colony counting are the clustering of colonies and edge effects. By using standard image analysis and implementing an inflection point algorithm, these problems were greatly diminished. The resulting system was compared with two manual colony counts, as well as with automated counts with the Oxford Optronix ColCount colony counter for cell lines V79 and HaCaT.We have developed a cheaper and more efficient device, which employs a flat bed scanner to image 12 60-mm petri dishes at a time. Two major problems in automated colony counting are the clustering of colonies and edge effects. By using standard image analysis and implementing an inflection point algorithm, these problems were greatly diminished. The resulting system was compared with two manual colony counts, as well as with automated counts with the Oxford Optronix ColCount colony counter for cell lines V79 and HaCaT.ResultsComparisons assuming the manual counts to be correct showed that our automatic counter was slightly more accurate than the commercial unit.Comparisons assuming the manual counts to be correct showed that our automatic counter was slightly more accurate than the commercial unit.ConclusionsAs a whole, our automated colony counter performed significantly better than the commercial unit with regard to processing time, cost and accuracy. © 2004 Wiley-Liss, Inc.As a whole, our automated colony counter performed significantly better than the commercial unit with regard to processing time, cost and accuracy. © 2004 Wiley-Liss, Inc.