Automatic classification of tuberculosis bacteria using neural network

Rulaningtyas, Riries; Suksmono, Andriyan Bayu; Mengko, Tati L. R.

doi:10.1109/iceei.2011.6021502

Cited by 36 publications

(17 citation statements)

References 2 publications

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“…In Rulaningtyas et al (2011), image segmentation, shape features and an ANN classifier are used in a two-class MM classification task. In this work, 75 MM images are used for training the classifier, 25 images are used for testing, and finally a mean square error of 0.0368% is obtained as the overall evaluation of the classification result.…”

Section: Overview Of MM Classificationmentioning

confidence: 99%

“…Beneficial AMs can help farmers to increase the agricultural yields, for Shabtai et al (1996), Ronen et al (2002) IM 1998 Gerlach SR et al Gerlach et al (1998) IM 2001 Lee MS et al Lee et al (2001) IM 2002 Lomander A et al Lomander et al (2002) IM 2002 Park JP et al Park et al (2002) IM 2007 Lecault V et al Lecault et al (2007) IM 2011 Yu B et al Yu et al (2011) MM 1992 Reichl U et al Reichl et al (1992) MM 1994 Pichon D et al Pichon et al (1994) MM 1996 Oh B et al Oh et al (1996) MM 1997 Tamura S et al Tamura et al (1997) MM 1998 Veropoulos K et al Veropoulos et al (1998) MM 1998 Wit P et al Wit and Busscher (1998) MM 1999 Kay JW et al Kay et al (1999) MM 2000 Khutlang R et al Khutlang et al (2009Khutlang et al ( , 2010a MM 2011 Rulaningtyas R et al Rulaningtyas et al (2011) MM 2011 Pangilinan C et al Pangilinan et al (2011) MM 2011-2012Osman MK et al Osman et al (2011a, b, 2012 MM 2012 Chang J et al Chang et al (2012b) MM 2012-2016 Priya E et al Priya et al (2012), Srinivasan (2015a, b, 2016) Culverhouse et al (1994Culverhouse et al ( , 1996Culverhouse et al ( , 2000Culverhouse et al ( , 2003Culverhouse et al ( , 2006a WM 1995 Thiel S et al Thiel and Davies (1995), …”

Section: Application Domain Introductionmentioning

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: Overview Of MM Classificationmentioning

confidence: 99%

Section: Application Domain Introductionmentioning

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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“…Medical microorganisms (MMs) shown in the third row of Table 1 are investigated for prevention, diagnosis, and treatment of infectious diseases. For example, Rulaningtyas et al present in [12] an approach for automatic classification of tuberculosis bacteria. For this, the authors use seven geometrical characteristics and neural networks for classification.…”

Section: Classification Of Microorganismsmentioning

confidence: 99%

Application of content-based image analysis to environmental microorganism classification

Shirahama

Grzegorzek

2015

Biocybernetics and Biomedical Engineering

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“…Medical Microorganisms (MMs) are investigated for prevention, diagnosis, and treatment of infectious diseases. For example, image segmentation, geometrical features and neural networks are used to detect tuberculosis bacteria [19]. In [24], a system for recognition of MMs is developed, where features are extracted by applying the Gabor-based wavelet method to image segmentation results, and a 3-D morphology-based recognition technique is used to identify the classes of MMs.…”

Section: Related Workmentioning

confidence: 99%

Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

Shirahama

Grzegorzek

2015

Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval

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Environmental Microorganisms (EMs), such as Epistylis and Rotifera, are very tiny living beings in human environments and decompose pollutants as their nutrition. The classification of EMs plays a fundamental role for establishing sustainable ecosystem. However, this is traditionally done by molecular methods using DNA or RNA analysis, or morphological methods using microscopes. These methods require huge monetary costs or manual efforts. In this paper, we propose an EM classification method by directly processing microscopic images. We especially address two problems: The first is a small training dataset problem. Because EM samples are collected from unstable real-world environments, it is difficult to stably obtain a large number of EM images. To effectively represent scarce training images, we use Sparse Coding (SC) which extracts sufficient local features from an image and reconstructs it by a sparse linear combination of bases. The second is a noisy image problem. Because EM samples are often found in dirty regions, their images often show a noisy background. To overcome this, we use Weakly Supervised Learning (WSL) which jointly performs the localisation and classification of EMs by examining the local information in training images. Experimental results on real-world images show the effectiveness and potential of our system.

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Automatic classification of tuberculosis bacteria using neural network

Cited by 36 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

Application of content-based image analysis to environmental microorganism classification

Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

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