A Novel Methodology for Automatic Bacterial Colony Counter

Kaur, Gurpreet; Sethi, Poonam

doi:10.5120/7704-1061

Cited by 8 publications

(6 citation statements)

References 18 publications

(13 reference statements)

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“…( 2001 ), Chunhachart and Suksawat ( 2016 ), Gupta et al. ( 2012 ), Sethi and Yadav ( 2012 ), Kaur and Sethi ( 2012 ), Nayak et al. ( 2010 ), Pernthaler et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…( 1997 ), Sotaquira et al. ( 2009 ), Maretić and Lacković ( 2017 ), Chunhachart and Suksawat ( 2016 ), Kaur and Sethi ( 2012 ), Payasi and Patidar ( 2017 ), global thresholding is applied for bacteria enumeration. The RGB image is firstly converted to YCbCr and Lab color spaces in Sotaquira et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…An adaptive median filter (Gupta et al. ( 2012 ), Kaur and Sethi ( 2012 )), a‘flatten filter’ (Trujillo et al. ( 2001 )) or a top-hat algorithm (Pernthaler et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…Besides, a combination method of Gaussian low-pass filter, simple symmetric moving average filter and median filter is applied for noise removal in Maretić and Lacković ( 2017 ). After thresholding, the morphological operations are applied for image enhancement (Chunhachart and Suksawat ( 2016 ), Kaur and Sethi ( 2012 )), and the detected circles are used for counting the number of colonies that appeared on the selected region. The detection result is shown in Fig.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

See 3 more Smart Citations

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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“…( 2001 ), Chunhachart and Suksawat ( 2016 ), Gupta et al. ( 2012 ), Sethi and Yadav ( 2012 ), Kaur and Sethi ( 2012 ), Nayak et al. ( 2010 ), Pernthaler et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…An adaptive median filter (Gupta et al. ( 2012 ), Kaur and Sethi ( 2012 )), a‘flatten filter’ (Trujillo et al. ( 2001 )) or a top-hat algorithm (Pernthaler et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

Section: Bacteria Counting Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…In our preliminary experiments we also tested other noise removal methods but, as expected, median filtering turned out to be the most suitable. Below the approximate size of the work area of the median filter objects get eliminated, which is sometimes regarded as a disadvantage [16]. This behavior, however, can also be used intentionally to exclude microcolonies.…”

Section: Plos Onementioning

confidence: 99%

Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Hogekamp

Hogekamp²,

Stahl

2020

PLoS ONE

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Automated colony counting methods have long been known in Microbiology. Numerous methods for automated image analysis have been described and a wide range of commercial products exists. Known advantages are saving cost by reducing enumeration time, automatic documentation, reproducibility, and operator independence. Still, even today the realization of all advantages of automated image analysis makes it necessary to either invest in an expensive, high performance commercial system, or to acquire expert knowledge in image processing. This is a considerable obstacle for many laboratories, and the reason why manual colony counting is still done frequently. This article describes an easy to apply automatic colony counting system-including suggestions for sample preparation-that can be put into operation with basic knowledge of image processing and low budget.

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Automatic Counting and Classification of Bacterial Colonies Using Hyperspectral Imaging

Yoon

Lawrence

Park

2015

Food Bioprocess Technol

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A Novel Methodology for Automatic Bacterial Colony Counter

Cited by 8 publications

References 18 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

Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Automatic Counting and Classification of Bacterial Colonies Using Hyperspectral Imaging

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