A survey for the applications of content-based microscopic image analysis in microorganism classification domains
文献类型：期刊论文
| 作者 | Li C(李晨); Xu, Ning; Wang K(王锴)
|
| 刊名 | Artificial Intelligence Review
 |
| 出版日期 | 2017 |
| 页码 | 1-70 |
| 关键词 | Microorganism classification Content-based microscopic image analysis Feature extraction Classifier design |
| ISSN号 | 0269-2821 |
| 产权排序 | 2 |
| 通讯作者 | Wang K(王锴) ; Xu, Ning |
| 中文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/20809]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 作者单位 | 1.Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110169, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.School of Arts and Design, Liaoning Shihua University, Fushun, 113001, China |
| 推荐引用方式 GB/T 7714 | Li C,Xu, Ning,Wang K. A survey for the applications of content-based microscopic image analysis in microorganism classification domains[J]. Artificial Intelligence Review,2017:1-70. |
| APA | Li C,Xu, Ning,&Wang K.(2017).A survey for the applications of content-based microscopic image analysis in microorganism classification domains.Artificial Intelligence Review,1-70. |
| MLA | Li C,et al."A survey for the applications of content-based microscopic image analysis in microorganism classification domains".Artificial Intelligence Review (2017):1-70. |
入库方式： OAI收割
来源：沈阳自动化研究所
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