Automated Phytoplankton Analysis by a Pattern Recognition Method

Uhlmann, Dirk; Schlimpeet, O.; Uhlmann, W.

doi:10.1002/iroh.19780630417

Cited by 14 publications

(8 citation statements)

References 2 publications

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“…In Uhlmann et al (1978), a WM classification approach is proposed, where image denoising, shape feature extraction and geometrical thresholds are used for a five-class classification task. In this work, 15 WMs of each class are used for calculating the thresholds, five WMs of each class are used for testing, finally an accuracy of 100% is achieved.…”

Section: Original Methodsmentioning

confidence: 99%

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

Wang

2017

Artif Intell Rev

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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 semior 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 devel-

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Section: Original Methodsmentioning

confidence: 99%

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

Wang

2017

Artif Intell Rev

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“…Several automatic methods have actually been developed for morphological identification. Computer vision has been used to identify phytoplankton cells and colonies (Uhlmann et al, 1978), dinoflagellates (Culverhouse et al, 1996), cyanobacteria (Walker and Kumagai, 2000) and diatoms (Fischer and Bunke, 2001) as well as zooplankton (Benfield et al, 1998). Flow cytometry has also been developed as an identification method (Dubelaar and Jonker, 2000).…”

Section: Introductionmentioning

confidence: 99%

Toxic and non-toxic Nodularia strains can be distinguished from each other and from eukaryotic algae with chlorophyll fluorescence fingerprinting

et al. 2009

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“…1;Schiller, 1918), a synonym of P. redfieldi Bursa (Toriumi, 1980) shape and often constitutes a large portion of red tide phytoplankton in coastal areas of Japan (Adachi, 1972;Toriumi, 1979;1980), its automatic identification was chosen as the subject of this study. Experimental systems for automated identification and counting of leucocytes by image analysis were made by Prewitt and Mendelsohn (1966) and by Preston (1962), while automated identification by image analysis of phytoplankton species cultured in the laboratory was reported by Uhlmann et al (1978) and for zooplankton species by Jeffries et al (1981Jeffries et al ( , 1984.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the most important constituents of red tides are dinoflagellates and diatoms (Bainbridge, 1956;Wood, 1965). Since one of these dinoflagellates, Prorocentrurn triestinum Schiller (Fig.…”

Section: Introductionmentioning

confidence: 99%

Automated identification of red tide phytoplanktonProrocentrum triestinum in coastal areas by image analysis

Tsuji

Nishikawa

1984

Journal of the Oceanographical Society of Japan

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An image analyzing system was developed for automated identification and cell counting of a red tide phytoplankton, Prorocentrum triestinum, in coastal areas. Using an interference filter, a photograph of phytoplankton was taken through a fluorescence microscope. The photograph was then fed into an image analyzing system consisting of an image sensor, a front mini-computer, and a host computer. The system identified P. trlestinum as effectively as the conventional method in one twentieth of the time required by the latter. The system was used to examine preserved samples collected from P. triestinum-dominated red tides in Tokyo Bay. It was found that there were two phytoplankton populations in the red tide: P. triestinum and a smaller phytoplankton.

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Automated Phytoplankton Analysis by a Pattern Recognition Method

Cited by 14 publications

References 2 publications

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

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

Toxic and non-toxic Nodularia strains can be distinguished from each other and from eukaryotic algae with chlorophyll fluorescence fingerprinting

Automated identification of red tide phytoplanktonProrocentrum triestinum in coastal areas by image analysis

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