Comparison of image analysis software packages in the assessment of adhesion of microorganisms to mucosal epithelium using confocal laser scanning microscopy

We have compared current image analysis software packages in order to find the most useful one for assessing microbial adhesion and inhibition of adhesion to tissue sections. We have used organisms of different sizes, the bacterium Helicobacter pylori and the yeast Candida albicans. Adhesion of FITC-labelled H. pylori and C. albicans was assessed by confocal microscopy. Four different Image analysis software packages, NIH-Image, IP Lab, Image Pro+, and Metamorph, were compared for their ability to quantify adhesion of the two organisms and several quantification methods were devised for each package. For both organisms, the dynamic range that could be detected by the software packages was 1×106?1×109 cells/ml. Of the four software packages tested, our results showed that Metamorph software, using our ?Region of Interest? method, with the software's ?Standard Area Method? of counting, was the most suitable for quantifying adhesion of both organisms because of its unique ability to separate clumps of microbial cells. Moreover, fewer steps were required. By pre-incubating H. pylori with the glycoconjugate Lewis b-HSA, an inhibition of binding of 48.8% was achieved using 250 ?g/ml Lewis b-HSA. The method we have devised using Metamorph software, provides a simple, quick and accurate way of quantifying adhesion and inhibition of adhesion of microbial cells to the epithelial surface of tissue sections. The method can be applied to organisms ranging in size from small bacteria to larger yeast cells

Whole Slide Imaging for High-Throughput Monitoring of Microbial Growth and Its Application to Rapid Antimicrobial Susceptibility Testing

Archival abstract submitte

An Automated System for Rapid Non-Destructive Enumeration of Growing Microbes

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.We present a new technology and platform that uses digital imaging of cellular autofluorescence to detect and enumerate growing microcolonies many generations before they become visible to the eye. The data presented demonstrate that the method preserves the viability of the microcolonies it detects, thus enabling generation of pure cultures for microbial identification. While visual colony counting detects Escherichia coli colonies containing about 5x10(6) cells, the new imaging method detects E. coli microcolonies when they contain about 120 cells and microcolonies of the yeast Candida albicans when they contain only about 12 cells. We demonstrate that digital imaging of microcolony autofluorescence detects a broad spectrum of prokaryotic and eukaryotic microbes and present a model for predicting the time to detection for individual strains. Results from the analysis of environmental samples from pharmaceutical manufacturing plants containing a mixture of unidentified microbes demonstrate the method's improved test turnaround times.This work demonstrates a new technology and automated platform that substantially shortens test times while maintaining key advantages of the current methods

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

Intelligent computational system for colony-forming-unit enumeration and differentiation

Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes 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 pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nível mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo às adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço

การประมวลผลภาพและประยุกต์วิธี SVM ในการนับจำนวนและวัดขนาด Staphylococci จากกล้องจุลทรรศน์

บทคัดย่อการนับจำนวนแบคทีเรียและวัดขนาดด้วยกล้องจุลทรรศน์มีความจำเป็นในงานตรวจสอบและการวิจัยทางจุลชีววิทยา แบคทีเรียเรียสแตฟิโลคอคไคเป็นกลุ่มแบคทีเรียที่มีความสำคัญทั้งในอุตสาหกรรมอาหารและโรงพยาบาล แต่ในการนับจำนวนแบคทีเรียต้องอาศัยผู้เชี่ยวชาญในการแยกแยะระหว่างโคโลนีของแบคทีเรียที่ต้องการตรวจสอบและสิ่งอื่นๆ และต้องนับไม่ต่ำกว่า 10 บริเวณที่ส่องกล้อง ซึ่งต้องใช้เวลาและส่งผลต่อสายตาของผู้นับอย่างมาก งานวิจัยนี้จึงนำเสนอซอฟท์แวร์ Micros-Staph ที่พัฒนาขึ้นเพื่อช่วยในการนับจำนวนและวัดขนาดแบคทีเรียที่มีรูปร่างกลมขนาดเล็กนี้ โดยใช้เทคนิคต่างๆมาสกัดลักษณะเด่นหลายประการของโคโลนีจากภาพเพื่อช่วยในการประมวลผลภาพ จากนั้นนำลักษณะเด่นเหล่านั้นมาใช้ในการแบ่งแยกโคโลนีออกจากสัญญาณรบกวนต่างๆด้วย Support Vector Machine (SVM)ทำให้ง่ายต่อการนับจำนวนที่แท้จริง ส่วนการวัดขนาดของเซลล์ทำโดยการเปรียบเทียบจำนวนของพิกเซลจากภาพถ่ายที่กำลังขยายเดียวกันของสเตจไมโครมิเตอร์กับพื้นที่ที่เล็กที่สุดของกล่องครอบภาพหลังการหมุนแกน ผลการทดลองแสดงให้เห็นว่าลักษณะเด่นที่ใช้ในการแบง่ แยกระหว่างโคโลนีและสัญญาณรบกวนนั้นสามารถใช้แบ่งแยกได้อย่างมีประสิทธิภาพ และผลการนับที่ได้มีความใกล้เคียงกับผลการนับจากผู้เชี่ยวชาญสูง เนื่องจากจำนวนที่นับได้โดยซอฟแวร์และผู้เชี่ยวชาญมีความสัมพันธ์เป็นเส้นตรงค่าสหสัมพันธ์ (r2) เป็น 0.99 สำหรับภาพกำลังขยาย 400 เท่าและ 0.98 สำหรับภาพกำลังขยาย 1000 เท่า นอกจากนี้การนับจำนวนแบคทีเรยสแตฟิโลคอคไคด้วย Micros-Staph ที่นำเสนอนี้สามารถลดเวลาในการนับได้มากกว่าครี่งหนึ่งของการนับโดยผู้เชี่ยวชาญ ขนาดของเซลล์ที่วัดได้โดยซอฟท์แวร์มีเส้นผ่านศูนย์กลางอยู่ในช่วง 0.5 – 0.9 ไมครอน ซึ่งสัมพันธ์กับขนาดจริงของแบคทีเรียคำสำคัญ: แบคทีเรีย สแตฟิโลคอคไค การนับจำนวนโคโลนี การนับด้วยล้องจุลทรรศน์ การประมวลผลภาพ วิธีหาขอบวัตถุ Support Vector MachineAbstractMicroscopic count and size measurement of bacteria are necessary for microbiological work. Staphylococci are one of the most frequently found bacteria in food industry and hospitals. However, an expert is needed for such count. He needs to perform at least 10 visual counts, which cause eye strain and are time consuming. This research proposes the developed software, namely Micros-Staph, to aid in the microscopic count of these bacteria as well as the size measurement of the individual cell using extracted features from image processing techniques. These features are used to train Support Vector Machine (SVM) to differentiate the bacterial colony from noises in the images. Therefore, the number of colony can be easily counted. The cell size is obtained by comparing the number of pixels on the stage micrometer with the cell bounding box at the smallest area found after angle rotation. The experimental results show that the extracted features can be used to count the number of colony effectively. The number of colony counted by SVM provides a linear relationship with those counted by the experts at the correlation value (r2) of 0.99 and 0.98 for the images captured with 400 and 1,000 times of magnification, respectively. Additionally, the time of microscopic count spent by the Micros-Staph software is reduced by more than half. The cell size measured by the software is 0.5 – 0.9 μm in diameter which is absolutely correlated to the actual cell size of staphylococci.Keywords: Staphylococcus, Bacteria, Colony Count, Microscopic Count, Image Processing, Canny Edge Detection, Support Vector Machine

Analysis by confocal laser scanning microscopy of the MDPB bactericidal effect on S. mutans biofilm CLSM analysis of MDPB bactericidal effect on biofilm

Since bacteria remain in the dentin following caries removal, restorative materials with antibacterial properties are desirable to help maintaining the residual microorganisms inactive. The adhesive system Clearfil Protect Bond (PB) contains the antibacterial monomer 12-methacryloyloxydodecylpyridinium bromide (MDPB) in its primer, which has shown antimicrobial activity. However, its bactericidal effect against biofilm on the dentin has been little investigated. Objective: The aim of this study was to analyze by confocal laser scanning microscopy (CLSM) and viable bacteria counting (CFU) the MDPB bactericidal effect against S. mutans biofilm on the dentin surface. Material and methods: Bovine dentin surfaces were obtained and subjected to S. mutans biofilm formation in BHI broth supplemented with 1% (w/v) sucrose for 18 h. Samples were divided into three groups, according to the primer application (n=3): Clearfil Protect Bond (PB), Clearfil SE Bond, which does not contain MDPB, (SE) and saline (control group). After the biofilm formation, Live/ Dead stain was applied directly to the surface of each sample. Next, 10 µL of each primer were applied on the samples during 590 s for the real-time CLSM analysis. The experiment was conducted in triplicate. The primers and saline were also applied on the other dentin samples during 20, 90, 300 and 590 s (n=9 for each group and period evaluated) and the CFU were assessed by colonies counting. Results: The results of the CLSM showed that with the Se application, although non-viable bacteria were detected at 20 s, there was no increase in their count during 590 s. In contrast, after the PB application there was a gradual increase of non-viable bacteria over 590 s. Conclusions: The quantitative analysis demonstrated a significant decrease of S. mutans CFU at 90 s PB exposure and only after 300 s of Se application. Protect Bond showed an earlier antibacterial effect than Se Bond