Image analysis method for the rapid counting of Saccharomyces cerevisiae cells

Costello, Peter J.; Monk, Paul R.

doi:10.1128/aem.49.4.863-866.1985

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…( 2010 ), thresholding is applied for fungi (Cross and Kenerley ( 2004 ), Packer and Thomas ( 1990 )), mycelia (Tucker et al. ( 1992 )), yeast (Costello and Monk ( 1985 )) and protozoan (Brown et al. ( 1989 ), Dias et al.…”

Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

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A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Zhang

Rahaman

et al. 2021

Artif Intell Rev

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Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

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Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

“…( 2003 )) counting. In Costello and Monk ( 1985 ), the yeast strains are counted by adjusting the gray-level of the images with the presence of high cell destinies. In Brown et al.…”

Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Zhang

Rahaman

et al. 2021

Artif Intell Rev

View full text Add to dashboard Cite

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“…Indeed, also previous work on image analysis applied to laboratory yeast samples is based on a homogeneous image background (Costello and Monk, 1985;Huls et al, 1992;Pons et al, 1993;Thomas and Paul, 1996;Tibayrenc et al, 2011). This precondition facilitates the application of standard image-processing operations as for example a threshold operation.…”

Section: Introductionmentioning

confidence: 99%

Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Belini

Caurin

Wiedemann

et al. 2017

Braz. J. Chem. Eng.

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This paper addresses some key issues related to the automation of fermentation process analysis in the context of industrial-scale ethanol production from sugarcane substrates. As the current methods for the determination of cell density and viability are time consuming and laborious, high resolution in situ microscopy (0.5µm) is proposed as a promising alternative. Laboratory-scale experiments presented here show that this imaging technique allows automatic, on-line, and real-time monitoring of yeast cells suspended in sugarcane molasses used in the ethanol industry. In particular, the feasibility of cell concentration measurements of Saccharomyces cerevisiae SA-1 in industrial sugarcane molasses is demonstrated. Automated concentration measurements exhibit a linear correlation with manual reference values using a Neubauer chamber from 3×10 6 cells/mL up to a saturation level at approximately 2×10 8 cells/mL. Furthermore, it was demonstrated that the microscopic resolution of this technique, combined with its large statistics, allows a morphological assessment of the size, shape and some internal structures of the yeast cells. On average, the accuracy of the algorithm´s yeast cells classification was 0.80. The results obtained suggest that the ISM is a suitable tool to perform in-line sugarcane fermentation monitoring.

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Control of a thiobacillus denitrificans bioreactor using machine vision

Camp

Sublette

1992

Biotech & Bioengineering

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A PC-based machine vision system has been used to continuously monitor changes in biomass concentration and to control the undesirable production of colloidal elemental sulfur (a reactor upset condition due to an excessive concentration of inhibitory sulfide substrate) in a bioreactor containing Thiobacillus denitrificans. A field of view of a video camera was established which contained regions of different background lighting. Mean values of the distribution of red, green, and blue intensity components within corresponding regions of a digital image image captured from the camera were used to monitor color changes associated with changes in biomass concentration, and to determine if the reactor was in an upset condition. The ration of red to blue intensity components was an important parameter in detecting the formation of an elemental sulfur precipitant. Using a stepper motor-driven pressure regulator, intelligent process control was performed by altering the hydrogen sulfide feed flow rate setpoint on the vision system measurements.

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Image analysis method for the rapid counting of Saccharomyces cerevisiae cells

Cited by 13 publications

References 3 publications

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Control of a thiobacillus denitrificans bioreactor using machine vision

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