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Artificial Intelligence Review

, Volume 51, Issue 4, pp 577–646 | Cite as

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

  • Chen Li
  • Kai Wang
  • Ning Xu
Article

Abstract

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 semi- or 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 development trend in this interdisciplinary field. In addition, the frequently-used CBMIA methods are further analysed to find technological common points and potential reasons.

Keywords

Microorganism classification Content-based microscopic image analysis Feature extraction Classifier design 

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Chen Li
    • 1
  • Kai Wang
    • 2
  • Ning Xu
    • 3
  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  3. 3.School of Arts and DesignLiaoning Shihua UniversityFushunChina

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