Skip to main content

A New Content-Based Image Retrieval System for SARS-CoV-2 Computer-Aided Diagnosis

  • Conference paper
  • First Online:
Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021) (MICAD 2021)

Abstract

Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system’s feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad, H., Khan, M., Yousaf, A., Ghuffar, S., Khurshid, K.: Deep learning: a breakthrough in medical imaging. Curr. Med. Imaging 16(8), 946–956 (2020)

    Article  Google Scholar 

  2. Anavi, Y., Kogan, I., Gelbart, E., Geva, O., Greenspan, H.: Visualizing and enhancing a deep learning framework using patients age and gender for chest X-ray image retrieval. In: Proceedings of the SPIE on Medical Imaging, vol. 9785, p. 978510 (2016)

    Google Scholar 

  3. Baur, C., Albarqouni, A., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI, pp. 311–319 (2017)

    Google Scholar 

  4. Camalan, S., et al.: OtoMatch: content-based eardrum image retrieval using deep learning. PLoS ONE 15(5), art. no. e0232776 (2020)

    Google Scholar 

  5. Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions. CVPR, pp. 1800–1807 (2017)

    Google Scholar 

  6. Gu, Z., et al.: Ce-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)

    Article  Google Scholar 

  7. Hamidinekoo, A., Denton, E., Honnor, K., Zwiggelaar, R.: An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 11513, art. no. 1151319 (2020)

    Google Scholar 

  8. Haq, N., Moradi, M., Wang, Z.: A deep community based approach for large scale content based X-ray image retrieval. Med. Image Anal. 68, art. no. 101847 (2021)

    Google Scholar 

  9. Hyvonen, V.: Fast nearest neighbor search through sparse random projections and voting. BigData, pp. 881–888 (2016)

    Google Scholar 

  10. Lin, M., Chen, Q., Yan, S.: Network in Network. ICLR (Poster) (2014)

    Google Scholar 

  11. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  12. Liu, X., Tizhoosh, H., Kofman, J.: Generating binary tags for fast medical image retrieval based on convolutional nets and Radon transform. In: Proceedings of the International Joint Conference on Neural Networks (2016)

    Google Scholar 

  13. Muller, H., Unay, D.: Retrieval from and understanding of large-scale multi-modal medical datasets: a review. IEEE Trans. Multimedia 19(9), art. no. 7984864, 2093–2104 (2017)

    Google Scholar 

  14. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  15. Shah, A., Conjeti, S., Navab, N., Katouzian, A.: Deeply learnt hashing forests for content based image retrieval in prostate MR images. In: Proceedings of the SPIE on Medical Imaging, vol. 9784, p. 978414 (2016)

    Google Scholar 

  16. Swati, Z., et al.: Content-based brain tumor retrieval for MR images using transfer learning. IEEE Access 7, art. no. 8611216, 17809–17822 (2019)

    Google Scholar 

  17. Tong, N., Gou, S., Yang, S., Ruan, D., Sheng, K.: Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med. Phys. 45(10), 4558–4567 (2018)

    Article  Google Scholar 

  18. Yu, Y., Li, M., Liu, L., Li, Y., Wang, J.: Clinical big data and deep learning: applications, challenges, and future outlooks. Big Data Mining Anal. 2(4), art. no. 8787233 288–305 (2019)

    Google Scholar 

Download references

Acknowledgments

This work was funded by ANID FONDEF grant 19I10023, ANID FONDECYT grant 11170475, ANID Basal Project FB0008, and ANID PIA/APOYO AFB180002. Dr. Mendoza acknowledges support from ANID Fondecyt grant 1200211.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcelo Mendoza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Molina, G. et al. (2022). A New Content-Based Image Retrieval System for SARS-CoV-2 Computer-Aided Diagnosis. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_33

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3880-0_33

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3879-4

  • Online ISBN: 978-981-16-3880-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics