Recognition of airborne fungi spores in digital microscopic images

Perner, Petra; Perner, Horst; Jänichen, Silke; Buhring, A.

doi:10.1109/icpr.2004.1334592

Cited by 13 publications

(11 citation statements)

References 5 publications

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“…To this end, we have developed a semi-automated procedure [14] that allows acquisition of the shape information from the raw image data and learning of groups of shape-cases and general shape-cases. A more detailed description of the case-based object-matching unit can be found in Section VII B.…”

Section: International Journal Of Machine Learning and Computing Volmentioning

confidence: 99%

“…Objects are recognized in the microscopic image by a case-based object-recognition unit [14]. This unit has a case-base of shapes (case base_1) for fungi spores and determines on a similarity-based inference if there are objects in the image that have a similar shape as the ones stored in the case base.…”

Section: A the Architecturementioning

confidence: 99%

“…It can also happen that only part of the object appears in the image. Therefore, we decided to use a case-based object recognition procedure [14] for the detection of objects in the image.…”

Section: B Case-based Object Recognitionmentioning

confidence: 99%

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An Automated Probe Handling and Image Inspection System for the Recognition of Biological Hazardous Material in the Air

Perner¹

2018

IJMLC

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Abstract-Human beings are exposed every day to bio-aerosols in their personal and professional life. The European Commission has issued regulations for protecting employees in the workplace from biological hazards. Airborne fungi can be detected and identified by an image-acquisition and interpretation system. In this paper, we present recent results on the development of an automated image acquisition, sample handling and image-interpretation system for airborne fungi identification. We explained the application domain and described the development issues. The development strategy and the architecture of the system are described, and results are presented.Index Terms-Health monitoring, Microscopic image acquisition, microbiological sample handling, image analysis, image interpretation, case-based object recognition, case-based reasoning.

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Section: International Journal Of Machine Learning and Computing Volmentioning

confidence: 99%

Section: A the Architecturementioning

confidence: 99%

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An Automated Probe Handling and Image Inspection System for the Recognition of Biological Hazardous Material in the Air

Perner¹

2018

IJMLC

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“…In Perner (2006), Perner et al (2004), Sklarczyk et al (2007), a semi-automatic multi-class classification system is developed to distinguish six categories of airborne fungi spores, where edge detection is firstly applied, then the detected edges are used as features for building a model-based object recognition method. In the experiment, 60 test examples of each EM class are used, and finally an overall classification accuracy of 88% is obtained.…”

Section: Overview Of Em Classificationmentioning

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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“…An example of section of a slide for some specific tasks (Carrión et al 2004;Perner and Günther 2005;Perner et al 2004). However, such solutions can suffer from the same lengthy and difficult data acquisition and model building phases that hamper machine vision solutions generally.…”

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confidence: 99%

Using active learning to annotate microscope images of parasite eggs

2006

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Microscopic analysis forms an integral part of many scientific studies. It is a task which requires great expertise and care. However, it can often be an extremely repetitive and labourious task. In some cases many hundreds of slides may need to be analysed, a process that will require each slide to be meticulously examined. Machine vision tools could be used to help assist in just such repetitive and tedious tasks. However, many machine vision solutions involve a lengthy data acquisition phase and in many cases result in systems that are highly specialised and not easily adaptable. In this paper, we describe a framework that applies flexible machine vision techniques to microscope analysis and utilises active learning to help overcome the data acquisition and adaptability problems. In particular we investigate the potential of various aspects of our proposed framework on a particular real world microscopic task, the recognition of parasite eggs.

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Recognition of airborne fungi spores in digital microscopic images

Cited by 13 publications

References 5 publications

An Automated Probe Handling and Image Inspection System for the Recognition of Biological Hazardous Material in the Air

An Automated Probe Handling and Image Inspection System for the Recognition of Biological Hazardous Material in the Air

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

Using active learning to annotate microscope images of parasite eggs

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