Automatic classification of bacterial cells in digital microscopic images

Hiremath, P. S.; Bannigidad, Parashuram

doi:10.1117/12.853303

Cited by 67 publications

(48 citation statements)

References 5 publications

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“…However, many image segmentation and classification approaches are not as good as expected when they are applied to biomedical areas. Various segmentation and classification algorithms have been proposed, such as geometric feature extraction [1]. the combination of color pixel classification and color watershed [2], Bayesian approach to sub voxel tissue classification [3] and cluster analysis [4].…”

Section: Introductionmentioning

confidence: 99%

Microscopic Image Segementing and Classification with RBF Neural Network

Chen

2012

2012 Fourth International Symposium on Information Science and Engineering

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Segmenting and interesting objects from microscopic images and classifying microscopic images are very important for biomedical researching work, which help diagnosis and further biomedical research. However, conventional approaches don't behavior as well as expected when they are applied to solve the problem. We hence propose two methods, radial basis function neural network with fuzzy initialization and graph-based discrete approach, for microscopic image segmenting and classification. The results show that RBF neural network has advantages such as easy to configure and implement, and the training procedure being very fast. In addition, RBF neural network employs fuzzy mean algorithm to accelerate the procedure of parameters and structure selection. Meanwhile, graphed-based discrete approach, which depends on the general formulation of discrete functional regularization on weighted graph, can be used to address cellular extraction segmentation problem.

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Section: Introductionmentioning

confidence: 99%

Microscopic Image Segementing and Classification with RBF Neural Network

Chen

2012

2012 Fourth International Symposium on Information Science and Engineering

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“…Digital microscopic image analysis of spiral bacterial cell groups using segmentation based on thresholding, feature extraction by leading to 100% classification accuracy by using five geometric features and neural network and fuzzy classifiers [19]. In the present paper, the objectives is to enhance the segmentation accuracy by using active contour method and reduced the complexity by using only three geometric features and yet attain 100% classification accuracy by neural network and neuro fuzzy classifiers.…”

Section: Fig 1 Arrangement Of Spiral Bacterial Cell Groupsmentioning

confidence: 99%

Spiral Bacterial Cell Image Analysis using Active Contour Method

Hiremath¹,

Bannigidad²

2012

IJCA

Self Cite

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Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple subregions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The objective of the present study is to develop an automatic tool to identify and classify the different types of spiral bacterial cells in digital microscopic cell images using active contour method. Geometric features are used to identify the arrangement of spiral bacterial cells, namely, vibrio, spirillum and spirochete. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for bacterial classification by segmenting digital spiral bacterial cell images and extracting only three geometric features for cell classification using different classifiers, namely, 3 classifier, K-NN classifier, Neural Network classifier and Neuro Fuzzy classifiers. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method.

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“…In a serial work Hiremath and Bannigidad (2009, b, 2011a, b, 2012, three types of spiral EMS (bacterial) are classified using image segmentation, five shape features and six classification algorithms (3δ, k-NN1, k-NN3, k-NN5, ANN and fuzzy classifiers). In the experiment, 1280 images are used for testing, and an overall classification accuracy is achieved nearly 100%.…”

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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Automatic classification of bacterial cells in digital microscopic images

Cited by 67 publications

References 5 publications

Microscopic Image Segementing and Classification with RBF Neural Network

Microscopic Image Segementing and Classification with RBF Neural Network

Spiral Bacterial Cell Image Analysis using Active Contour Method

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

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