Abstract
SARS-CoV-2 has characteristics of wide contagion and quick propagation velocity. To analyse the visual information of it, we build a SARS-CoV-2 Microscopic Image Dataset (SC2-MID) with 48 electron microscopic images and also prepare their ground truth images. Furthermore, we extract multiple classical features and novel deep learning features to describe the visual information of SARS-CoV-2. Finally, it is proved that the visual features of the SARS-CoV-2 images which are observed under the electron microscopic can be extracted and analysed.
J. Zhang—Cofirst author. This work is supported by “National Natural Science Foundation of China” (No. 61806047), the “Fundamental Research Funds for the Central Universities” (Nos. N2019003 and N2019005), and the China Scholarship Council (No. 2017GXZ026396).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, N., et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223), 507–513 (2020)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Chu, D., et al.: Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia. Clinical Chemistry, January 2020
Cui, J., Li, F., Shi, Z.: Origin and evolution of pathogenic coronaviruses. Nature reviews. Microbiology 17(3), 181–192 (2019)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of ICPR 2005, pp. 886–893 (2005)
Gorbalenya, A., et al.: Severe acute respiratory syndrome-related coronavirus: the species and its viruses - a statement of the coronavirus study group. bioRxiv (2020)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Hui, D., et al.: The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - the latest 2019 novel coronavirus outbreak in Wuhan, China. Int. J. Infect. Dis. 91, 264–266 (2020)
Image, B.: The electron microscopic image of SARS-CoV-2. https://baike.baidu.com/item/2019
Kulwa, F., et al.: A State-of-the-art survey for microorganism image segmentation methods and future potential. IEEE Access 7(1), 100243–100269 (2019)
Li, C.: Content-based Microscopic Image Analysis. Logos Verlag Berlin GmbH, Gubener Street 47, Berlin, Germany (2016)
Li, C., Kulwa, F., Zhang, J., Li, Z., Xu, H., Zhao, X.: A review of clustering methods in microorganism image analysis. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technology in Biomedicine. AISC, vol. 1186, pp. 13–25. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49666-1_2
Li, C., Wang, K., Xu, N.: A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif. Intell. Rev. 51(4), 577–646 (2019)
Li, C., et al.: A brief review for content-based microorganism image analysis using classical and deep neural networks. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) ITIB 2018. AISC, vol. 762, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91211-0_1
Malik, Y., et al.: Emerging novel coronavirus (2019-nCoV)-current scenario, evolutionary perspective based on genome analysis and recent developments. Vet. Q. 40(1), 68–76 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
University, J.H.: Coronavirus COVID-19 global cases by the center for systems science and engineering (CSSE) at johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html
Acknowledgements
We thank B.E. Jiawei Zhang, due to his great work is considered as important as the first author in this paper. We also thank the websites which provide SARS-CoV-2 images.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, C., Zhang, J., Kulwa, F., Qi, S., Qi, Z. (2020). A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Features. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-60633-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60632-9
Online ISBN: 978-3-030-60633-6
eBook Packages: Computer ScienceComputer Science (R0)