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Structure of bacterial cell 

Structure of bacterial cell 

Source publication
Article
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The main objective of the present study is to develop an automatic tool to characterize the morphology of flagellar movements of bacterial cells in digital microscopic cell images. Geometric shape features are used to identify the different characteristics of bacterial cell flagellar movements, namely, monotrichous, lophotrichous, peritrichous and...

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Context 1
... anatomy of bacterial cell structures possess, cell wall, cell membrane and the protective gelatinous covering outside the cell wall known as capsule. Apart from this, some bacteria possess filamentous appendages, flagella and fimbriae, which protrude from the cell surface. Surface structures originate outside the cell membrane, sometimes being attached to it, and extend into the environment [14,16,17]. The structure of bacterial cell is shown in Fig.1. In this paper, the images of bacterial cells of flagella surface structures are considered for the identification and classification of the cells. In our study, the morphological flagellar movements, namely, monotrichous, lophotrichous, peritrichous, amphitrichous of bacterial cell structures are ...

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Citations

... 3. Automated concrete cracks detection for checking the integrity of the building structures [12]. 4. Healthcare studies where detection and precise localization of thin structures like flagella and the human sperm cell tail [13] can aid in numerous tasks such as the identification and classification of the bacterial cell characteristics [14] and morphology detection for fertility treatments [15] respectively. 5. Robotic manipulation of thin structures such as wires and cables [16]. ...
Conference Paper
Efficient detection of thin objects, from stationary or moving images, is significant in a variety of research areas. These research areas include but are not limited to electric power line detection systems, sperm tail detection for clinical sperm research, mooring lines detection, road-lane line detection for autonomous vehicles, and cracks detection for the integrity assessment of building structures. However, the detection of thin objects is a challenging computer vision task owing to the slimmer and less compact nature of these objects. Moreover, the complexity present in certain images, such as the background clutter, further adds to this problem of accurately detecting thin objects. In this work, we investigate a series of state-of-the-art deep learning detectors for thin objects’ detection. The detectors examined in this work were: EfficientDet, YOLOv5 and U-Net. The experimental results of this study reveal that generic state-of-the-art deep detectors are not suitable for detecting thin objects due to their reliance on coarse bounding boxes and/or excessive pixel-level computations while the application-specific detectors possess poor generalization capabilities and do not work accurately outside their domains. These empirical findings indicate the necessity of the identification of critical factors affecting thin objects detection and the subsequent design of a generic thin objects’ detector.