A survey for the applications of content-based microscopic image analysis in microorganism classification domains.
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| Title: | A survey for the applications of content-based microscopic image analysis in microorganism classification domains. |
|---|---|
| Authors: | Li, Chen1 (AUTHOR) lichen@bmie.neu.edu.cn, Wang, Kai2 (AUTHOR) alichen@bmie.neu.edu.cn, Xu, Ning3 (AUTHOR) |
| Source: | Artificial Intelligence Review. Apr2019, Vol. 51 Issue 4, p577-646. 70p. |
| Subjects: | Classification of microorganisms, Microscopy, Image analysis, Computer vision, Multiple intelligences, Interdisciplinary approach to knowledge |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: A survey for the applications of content-based microscopic image analysis in microorganism classification domains. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Chen%22">Li, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lichen@bmie.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Kai%22">Wang, Kai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> alichen@bmie.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Ning%22">Xu, Ning</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Apr2019, Vol. 51 Issue 4, p577-646. 70p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification+of+microorganisms%22">Classification of microorganisms</searchLink><br /><searchLink fieldCode="DE" term="%22Microscopy%22">Microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+intelligences%22">Multiple intelligences</searchLink><br /><searchLink fieldCode="DE" term="%22Interdisciplinary+approach+to+knowledge%22">Interdisciplinary approach to knowledge</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-017-9572-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 70 StartPage: 577 Subjects: – SubjectFull: Classification of microorganisms Type: general – SubjectFull: Microscopy Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Multiple intelligences Type: general – SubjectFull: Interdisciplinary approach to knowledge Type: general Titles: – TitleFull: A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Chen – PersonEntity: Name: NameFull: Wang, Kai – PersonEntity: Name: NameFull: Xu, Ning IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 51 – Type: issue Value: 4 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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