URLanisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. URLanism counting techniques are crucial in URLanism analysis, helping biologists and related researchers quantitatively analyze the URLanisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional URLanism manual counting methods are time consuming and subjective, which cannot be applied in large-scale applications. In order to improve this situation, image analysis-based URLanism counting systems are developed since 1980s, which consists of digital image processing, image segmentation, image classification and so on. Moreover, image analysis-based URLanism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of URLanism counting methods using digital image analysis. Firstly, the URLanisms are grouped as bacteria and other URLanisms. Then, the related articles are summarized based on image segmentation methods. Each part of articles are reviewed by time periods . Moreover, commonly used image processing methods for URLanism counting are summarized and analyzed to find technological common points. More than 142 papers are summarized in this article. In conclusion, this article can be referred to researchers to determine the development trend in URLanism countingfield and further analyze the potential applications
URLanisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. URLanism counting techniques are crucial in URLanism analysis, helping biologists and related researchers quantitatively analyze the URLanisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional URLanism 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 URLanism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based URLanism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of URLanism counting methods using digital image analysis. Firstly, the URLanisms are grouped as bacteria and other URLanisms. 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 URLanism 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 URLanism counting, and provides systematic suggestions for implementing integrated URLanism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.
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are time consuming and subjective, which cannot
, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to
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analysis-based URLanism counting systems are developed since
analysis is applied for URLanism counting since the
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so on. Moreover, image
suchlike. Image
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articles are reviewed by time periods
the article is reviewed by methodologies
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technological common
common technological
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142 papers are summarized
144 papers are outlined
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article can be referred to researchers to determine the development trend in URLanism countingfield and further analyze the potential applications
paper provides new ideas for the future development trend of URLanism counting, and provides systematic suggestions for implementing integrated URLanism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.