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      OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects

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          Abstract

          Counting circular objects such as cell colonies is an important source of information for biologists. Although this task is often time-consuming and subjective, it is still predominantly performed manually. The aim of the present work is to provide a new tool to enumerate circular objects from digital pictures and video streams. Here, I demonstrate that the created program, OpenCFU, is very robust, accurate and fast. In addition, it provides control over the processing parameters and is implemented in an in- tuitive and modern interface. OpenCFU is a cross-platform and open-source software freely available at http://opencfu.sourceforge.net.

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          Most cited references13

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          A simple and fast method for determining colony forming units.

          To develop a flexible and fast colony forming unit quantification method that can be operated in a standard microbiology laboratory. A miniaturized plating method is reported where droplets of bacterial cultures are spotted on agar plates. Subsequently, minicolony spots are imaged with a digital camera and quantified using a dedicated plug-in developed for the freeware program IMAGEJ. A comparison between conventional and minicolony plating of industrial micro-organisms including lactic acid bacteria, Eschericha coli and Saccharomyces cerevisiae showed that there was no significant difference in the results obtained with the methods. The presented method allows downscaling of plating by 100-fold, is flexible, easy-to-use and is more labour-efficient and cost-efficient than conventional plating methods. The method can be used for rapid assessment of viable counts of micro-organisms similar to conventional plating using standard laboratory equipment. It is faster and cheaper than conventional plating methods.
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            • Record: found
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            Automated Counting of Bacterial Colony Forming Units on Agar Plates

            Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates. A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups. To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.
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              Low-cost, high-throughput, automated counting of bacterial colonies.

              Research involving bacterial pathogens often requires enumeration of bacteria colonies. Here, we present a low-cost, high-throughput colony counting system consisting of colony counting software and a consumer-grade digital camera or document scanner. We demonstrate that this software, called "NICE" (NIST's Integrated Colony Enumerator), can count bacterial colonies as part of a high-throughput multiplexed opsonophagocytic killing assay used to characterize pneumococcal vaccine efficacy. The results obtained with NICE correlate well with the results obtained from manual counting, with a mean difference of less than 3%. NICE is also rapid; it can count colonies from multiple reaction wells within minutes and export the results to a spreadsheet for data processing. As this program is freely available from NIST, NICE should be helpful in bacteria colony enumeration required in many microbiological studies, and in standardizing colony counting methods.
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                Author and article information

                Journal
                10.1371/journal.pone.0054072
                1210.5502

                Computer vision & Pattern recognition,Quantitative & Systems biology
                Computer vision & Pattern recognition, Quantitative & Systems biology

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