Evaluation of an IUL Flash & Go Automated Colony Counter

An IUL Flash & Go automated colony counter was used to enumerate E. coli (ATCC 700728) colonies and its performance was compared with manual counting on spiral plates. A total of 85 plates were analyzed. Linear regression analysis and the log differences between the manual and automated counts were determined. The results were analyzed to evaluate the reliability and accuracy of the colony counter.  A correlation coefficient of 0.969, a slope of 0.932 and intercept of 0.25 all indicate a strong, linear relationship. The mean log value difference between the manual and Flash & Go count methods was -0.035. Of the 85 plates counted, 95% of the plates were within 0.15 log10 difference between the manual and Flash & Go automated counts. These results demonstrate that the Flash & Go automated colony counter is an effective, accurate and time saving alternative to the standard method of manual counting.      

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

Real-time bacterial microcolony counting using on-chip microscopy

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery

Unified framework for counting agriculture-related objects in digital images.

Abstract-Counting objects is an important activity in the daily routine of many areas of industry. This is particularly true in agriculture, in which objects like cells, microorganisms, seeds and other structures have to be quantified as a source of relevant information. This paper proposes a framework that aggregates three different algorithms into a single tool able to tackle a wide variety of counting problems that exist in the agriculture industry. The factor that brings all those algorithms together is the input by the user of some templates for the objects, which allows the resulting method to select the best option for those particular conditions. As a desirable side effect, problems related to resolution and scale dependencies that plagued those previous algorithms are mostly solved by this new approach.SIBGRAPI 2012

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

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

Facilitated endospore detection for Bacillus spp. through automated algorithm-based image processing

Bacillus spp. endospores are important dormant cell forms and are distributed widely in environmental samples. While these endospores can have important industrial value (e.g. use in animal feed as probiotics), they can also be pathogenic for humans and animals, emphasizing the need for effective endospore detection. Standard spore detection by colony forming units (CFU) is time-consuming, elaborate and prone to error. Manual spore detection by spore count in cell counting chambers via phase-contrast microscopy is less time-consuming. However, it requires a trained person to conduct. Thus, the development of a facilitated spore detection tool is necessary. This work presents two alternative quantification methods: first, a colorimetric assay for detecting the biomarker dipicolinic acid (DPA) adapted to modern needs and applied for Bacillus spp. and second, a model-based automated spore detection algorithm for spore count in phase-contrast microscopic pictures. This automated spore count tool advances manual spore detection in cell counting chambers, and does not require human overview after sample preparation. In conclusion, this developed model detected various Bacillus spp. endospores with a correctness of 85–89%, and allows an automation and time-saving of Bacillus endospore detection. In the laboratory routine, endospore detection and counting was achieved within 5–10 min, compared to up to 48 h with conventional methods. The DPA-assay on the other hand enabled very accurate spore detection by simple colorimetric measurement and can thus be applied as a reference method

Interactive/automated method to count bacterial colonies

O crescimento e manutenção de bactérias em placas de agar (placas de Petri) tem sido uma prática comum em microbiologia. O número de colónias numa cultura é normalmente contado manualmente para calcular a concentração de bactérias, no entanto, este processo é demorado, enfadonho e propenso a erros. A maioria dos sistemas automáticos de contagem, existentes na literatura; realizam a contagem de forma adequada quando as colónias estão bem espaçadas, são grandes, e de forma circular e têm um bom contraste com fundo. Quando estes pressupostos são violados, sistemas de análise de colónias automáticos podem rapidamente perder a fidelidade, precisão e utilidade. Para resolver os problemas acima, o objetivo deste trabalho é projetar e implementar um sistema centrado no software de baixo custo que aceita imagens gerais de câmaras digitais, para a deteção, bem como enumerar colónias de bactérias de uma forma totalmente automática. Um sistema interativo também é proposto para ultrapassar qualquer erro do sistema totalmente automático. Neste estudo foram consideradas 26 imagens, 21 delas obtidas na biblioteca existente e as outras 5 criadas desde o início. Os dois tipos de imagem, têm sistemas de captura diferente, consequentemente, os dois tipos de imagens são diferentes. O pré-processamento permite a construção de uma imagem apenas com a placa de Petri, removendo o ruído e o fundo. Esta etapa permite, também, a separação de imagem em duas partes, uma das quais contém a área central e a outra a zona da borda (anel), e prepara-as para a fase de segmentação. A segmentação permite a extração das colónias a partir da área central, como a área do anel. Nas imagens obtidas por este método, esta extração na zona central é realizado por um binarização com um valor fixo para o limiar. Na área de rebordo, é usado um binarização também, combinada com informações sobre o comprimento do eixo maior e menor, excentricidade e média das áreas. Na outra biblioteca, a segmentação é realizada utilizando um bottom-hat filtering, tanto na área central, como na zona do anel. Informações sobre o comprimento dos eixos maior e menor, excentricidade e áreas também são usados na área do anel. Depois disso, as colónias segmentadas são separados em duas imagens. Uma delas contendo as unidades de colónia e a outra as colónias em cluster. Esta separação é realizada através da excentricidade dos objetos. Para finalizar, o usuário escolhe o método de contagem. Para separar e contar as colónias em cluster, o sistema automático utiliza a watershed transformation e o sistema interativo utiliza os cliques do usuário. Os sistemas propostos são capazes de reduzir a mão-de-obra e tempo necessário para a contagem de colónias. O sistema automático proposto tem dificuldade em contar colónias na área do anel, fazendo com que o sistema não conte algumas colónias. O método interativo, corrige todos os problemas do método automático, produzindo resultados similares à contagem manual.The growth and maintenance of bacteria on agar plates (Petri dishes) has long been a common practice in microbiology. The number of colonies in a culture is usually counted manually to calculate the concentration of bacteria, however, this process is time-consuming, tedious and error prone. Most automatic counting systems, existing on the literature; perform adequately when the colonies are well spaced, large, and circular in shape and with good contrast from the background. When these assumptions are violated, most automatic colony analysis systems can rapidly lose reliability, accuracy and utility. To address the above problems, the goal of this study is to design and implement a cost-effective, software-centered system that accepts general digital camera images as its input, for detecting as well as enumerating bacterial colonies in a fully automatic manner. An interactive semi-automatic system is also proposed to overcome any error from fully automatic system. In this study were considered 26 images, 21 them obtained in the existing library and the other 5 created from the beginning. The two types of image, have capturing systems different, consequently the two types of images are different. The pre-processing allows the construction of an image only with the Petri dish, removing noise and the background. This step allows also, the separation of the image in two parts, one of them containing the central area and the other one the rim area, and prepares them to the segmentation stage. The segmentation enables the extraction of the colonies from the central area as the rim area. In the images obtained for this method, this extraction on central area is realized by a binarization with a fixed value for the threshold. In the rim area, a binarization is used too, combined with information about the major and minor axis length, eccentricity and areas from the image objects. In the other library, the segmentation is performed using a bottom-hat filtering in both the central area as in the rim area. Information about major and minor axis length, eccentricity and areas from the objects are also used in the rim area. After that, the colonies segmented are separated in two images. One of them containing the isolated colonies and the other one the clustered colonies. This separation is performed by the eccentricity of the objects. To finalize, the user chose the counting method. To separate and count the clustered colonies, the automatic system uses a watershed transformation and the interactive system uses the user´s input. The proposed systems are capable to reduce the manpower and time required for counting colonies. The proposed automatic system has difficulty counting colonies in the area of the rim, causing it to have a significant number of non-colonies counted. The interactive method, correct all the problems of the automatic method, producing results similar to the manual count