Digital image analysis of cocci bacterial cells using active contour method

Hiremath, P. S.; Bannigidad, Parashuram

doi:10.1109/icsip.2010.5697462

Cited by 9 publications

(13 citation statements)

References 9 publications

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“…Based on the four criteria in Table 1, the calculation methods of accuracy, sensitivity, specificity and precision are shown in Table 2. Some studies only used accuracy [64] or sensitivity [65] as evaluation metrics. However, few works considered multiple of them, such as sensitivity and specificity [66], and accuracy, sensitivity and specificity [67].…”

Section: Evaluation Criteriamentioning

confidence: 99%

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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Section: Evaluation Criteriamentioning

confidence: 99%

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In [53,55], an image analysis method used for cocci bacterial cell classification is proposed. In this method, the image segmentation is performed by the actived contour, and then five geometric features, which contain circularity, compactness, eccentricity, tortuosity and length-width ratio, are extracted for training and testing of the classifiers.…”

Section: Classification Tasksmentioning

confidence: 99%

“…The experimental results show that the neural network achieves the best performance. According to [53], the classification accuracy details of each class achieve by the neural network are 100%, 99%, 98%, 99%, 98%, and 99%, respectively.…”

Section: Classification Tasksmentioning

confidence: 99%

“…In [101], a method based on the image analysis technique and ANN is introduced to detect the Mycobacterium tuberculosis in the tissue section. Similar to [52,53], the same workflow is applied to classify three types of spiral bacterial cells: are vibrio, spirillum, and spirochete in [54]. Same as [52], 3σ, KNN, neural network, and fuzzy classifiers are applied in this study.…”

Section: Classification Tasksmentioning

confidence: 99%

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Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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“…Automated kiwifruit counting system [5] is developed by employing digital image analysis techniques, three simple counting methods and a minimum distance classifier based segmentation technique. An automatic identification and classification tool for the different types of cocci bacterial cells in digital microscopic cell images developed by using image analysis techniques along with K-NN and Neural Network classifiers [6]. Three dimensional measurements are enhanced using permanent staining and brightfield microscopy.…”

Section: Advanced Developments In Image Analysismentioning

confidence: 99%

Semantic Image Analysis for Intelligent Image Retrieval

Khodaskar

Ladhake

2015

Procedia Computer Science

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Image understanding and analysis is the most exciting and fastest-growing research areas in the computer vision. Recent computer vision technologies and algorithms are support efficient semantic image analysis and retrieval. Image analysis is deal with image representation, estimation formula, and sampling density. Image analysis at semantic level is result in automatic extraction of image descriptions as per human perception which ultimately bridge semantic gap between low-level visual features and the high-level concepts capturing the conveyed meaning. Vital semantic image information is basically retrieved from image content, mainly from meaningful image objects and their mutual relations. In this paper, we present Semantic analysis of image by knowledge driven approach , start with Image content analysis with respect to semantic concepts, design image database and knowledge base on the basis of semantic content and retrieval, presentation and modification of image reference database or knowledge base for knowledge delivery intention. Experimental result shows improvement in image retrieval performance and accuracy.

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Digital image analysis of cocci bacterial cells using active contour method

Cited by 9 publications

References 9 publications

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Semantic Image Analysis for Intelligent Image Retrieval

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