Monitoring micorbial morphogenetic changes in a fermentation process by a Self-Tuning Vision System (STVS)

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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“…In Shabtai et al. ( 1996 ), Embleton et al. ( 2003 ), a neural network is applied for fungus (Shabtai et al.…”

Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

“…( 2003 ), a neural network is applied for fungus (Shabtai et al. ( 1996 )) and phytoplankton (Embleton et al. ( 2003 )) counting.…”

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

et al. 2021

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“…Filamentous microorganisms such as Microthrix parvicella are responsible for bulking and foaming accidents [126,127] in activated sludge wastewater plants: such events are detrimental to the control of the plant, especially of the secondary settler. Some plant cells (Morinda citrifolia for example) [128] or yeast cells (Saccharomyces cerevisiae [129], Kluveyromyces marxianus [130], Aureobasidium pullulans [131,132]) can also exhibit filamentous morphologies.…”

Section: The Special Case Of Filamentous Speciesmentioning

confidence: 99%

Biomass Quantification by Image Analysis

Pons

Vivier

1999

Bioanalysis and Biosensors for Bioprocess Monitoring

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Microbiologists have always rely on microscopy to examine microorganisms. When microscopy, either optical or electron-based, is coupled to quantitative image analysis, the spectrum of potential applications is widened: counting, sizing, shape characterization, physiology assessment, analysis of visual texture, motility studies are now easily available for obtaining information on biomass. In this chapter the main tools used for cell visualization as well as the basic steps of image treatment are presented. General shape descriptors can be used to characterize the cell morphology, but special descriptors have been defined for filamentous microorganisms. Physiology assessment is often based on the use of fluorescent dyes. The quantitative analysis of visual texture is still limited in bioengineering but the characterization of the surface of microbial colonies may open new prospects, especially for cultures on solid substrates. In many occasions, the number of parameters extracted from images is so large that data-mining tools, such as Principal Components Analysis, are useful for summarizing the key pieces of information.

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“…In Ronen et al (2002), Shabtai et al (1996), a self-tuning vision system is proposed to classify two mainly forms of yeast-like fungus in a fermentation process, where image segmentation is first done, then shape features are extracted, lastly ANN and fuzzy logic technologies are combined for classification.…”

Section: Overview Of Im Classificationmentioning

confidence: 99%

“…Beneficial AMs can help farmers to increase the agricultural yields, for Shabtai et al (1996), Ronen et al (2002) IM 1998 Gerlach SR et al Gerlach et al (1998) IM 2001 Lee MS et al Lee et al (2001) IM 2002 Lomander A et al Lomander et al (2002) IM 2002 Park JP et al Park et al (2002) IM 2007 Lecault V et al Lecault et al (2007) IM 2011 Yu B et al Yu et al (2011) MM 1992 Reichl U et al Reichl et al (1992) MM 1994 Pichon D et al Pichon et al (1994) MM 1996 Oh B et al Oh et al (1996) MM 1997 Tamura S et al Tamura et al (1997) MM 1998 Veropoulos K et al Veropoulos et al (1998) MM 1998 Wit P et al Wit and Busscher (1998) MM 1999 Kay JW et al Kay et al (1999) MM 2000 Khutlang R et al Khutlang et al (2009Khutlang et al ( , 2010a MM 2011 Rulaningtyas R et al Rulaningtyas et al (2011) MM 2011 Pangilinan C et al Pangilinan et al (2011) MM 2011-2012Osman MK et al Osman et al (2011a, b, 2012 MM 2012 Chang J et al Chang et al (2012b) MM 2012-2016 Priya E et al Priya et al (2012), Srinivasan (2015a, b, 2016) Culverhouse et al (1994Culverhouse et al ( , 1996Culverhouse et al ( , 2000Culverhouse et al ( , 2003Culverhouse et al ( , 2006a WM 1995 Thiel S et al Thiel and Davies (1995), …”

Section: Application Domain Introductionmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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Monitoring micorbial morphogenetic changes in a fermentation process by a Self-Tuning Vision System (STVS)

Cited by 9 publications

References 8 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

Biomass Quantification by Image Analysis

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

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