Stalked protozoa identification by image analysis and multivariable statistical techniques

Amaral, A. L.; Ginoris, Yovanka Pérez; Nicolau, Ana; Coelho, Maria Alice Zarur; Ferreira, Eugénio C.

doi:10.1007/s00216-008-1845-y

Cited by 19 publications

(25 citation statements)

References 13 publications

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“…Crossed work Besides the related work above, the research of EM classification (Akiba and Kahui 2000;Amaral 2003;Amaral et al 1999Amaral et al , 2004Amaral et al , 2008Chen and Li 2008;Ferreira and Rasband 2012;Ginoris et al 2006Ginoris et al , 2007aGray et al 2002;Jenne et al 2001Jenne et al , 2002Jenne et al , 2003Kiranyaz et al 2011;Chen 2008, 2009;Motta et al 2001;Ruusuvuori et al 2008;Thiel and Davies 1995;Thiel andWiltshire 1995 in Sect. 3 andMM classification (Javidi et al 2006g) in Sect.…”

Section: Original Methodsmentioning

confidence: 99%

“…In the experimental phase, 14 protozoa species are tested, and an overall accuracy higher than 50% is obtained. Hereafter, the system is further updated in Amaral et al (2008), where an ANN classifier is built and obtains an overall classification accuracy near to 85%. In Fig.…”

Section: Overview Of Em Classificationmentioning

confidence: 99%

“…In Gray et al (2002), a one-class classification CBMIA system is used to detect alga for monitoring environmental quality, where image enhancing method is first used, then image (Amaral et al 2008) segmentation approach is applied to identify the EMs, finally the classification is carried out by decision values (threshold values).…”

Section: Overview Of Em Classificationmentioning

confidence: 99%

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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%

Section: Overview Of Em Classificationmentioning

confidence: 99%

Section: Overview Of Em Classificationmentioning

confidence: 99%

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

Wang

2017

Artif Intell Rev

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“…Currently, image analysis is primarily used under controlled laboratory conditions, where populations and individuals can be phenotyped to answer a variety of ecological and evolutionary questions [9, 11]. However, these techniques have also been successfully applied in natural systems, for instance, to identify plankton species in marine surveys [12, 13] and to monitor microorganisms in waste water treatment plants [14, 15]. The wealth of data produced by image and video analysis is both a blessing and a curse.…”

Section: Introductionmentioning

confidence: 99%

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

et al. 2017

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The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology.

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“…Some success with automatic classification of species was achieved with protists in activated wastewater sludge (Amaral et al. 2008) and with automated systems to analyze abundance and quantify trait distributions during large-scale marine monitoring schemes (e.g., Zoo/PhytoImage: http://www.sciviews.org/zooimage/, Bell and Hopcroft 2008). These show that such efforts are worthwhile even with challenging field-collected samples.…”

Section: Introductionmentioning

confidence: 99%

BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes

Pennekamp

Schtickzelle

Petchey³

2015

Ecology and Evolution

103

121

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Microbes are critical components of ecosystems and provide vital services (e.g., photosynthesis, decomposition, nutrient recycling). From the diverse roles microbes play in natural ecosystems, high levels of functional diversity result. Quantifying this diversity is challenging, because it is weakly associated with morphological differentiation. In addition, the small size of microbes hinders morphological and behavioral measurements at the individual level, as well as interactions between individuals. Advances in microbial community genetics and genomics, flow cytometry and digital analysis of still images are promising approaches. They miss out, however, on a very important aspect of populations and communities: the behavior of individuals. Video analysis complements these methods by providing in addition to abundance and trait measurements, detailed behavioral information, capturing dynamic processes such as movement, and hence has the potential to describe the interactions between individuals. We introduce BEMOVI, a package using the R and ImageJ software, to extract abundance, morphology, and movement data for tens to thousands of individuals in a video. Through a set of functions BEMOVI identifies individuals present in a video, reconstructs their movement trajectories through space and time, and merges this information into a single database. BEMOVI is a modular set of functions, which can be customized to allow for peculiarities of the videos to be analyzed, in terms of organisms features (e.g., morphology or movement) and how they can be distinguished from the background. We illustrate the validity and accuracy of the method with an example on experimental multispecies communities of aquatic protists. We show high correspondence between manual and automatic counts and illustrate how simultaneous time series of abundance, morphology, and behavior are obtained from BEMOVI. We further demonstrate how the trait data can be used with machine learning to automatically classify individuals into species and that information on movement behavior improves the predictive ability.

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Stalked protozoa identification by image analysis and multivariable statistical techniques

Cited by 19 publications

References 13 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

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes

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