Skip to main content
SpringerLink
Log in

Content-Based and Similarity-Based Querying for Broad-Usage Medical Image Retrieval

  • Conference paper
Book cover Advances in Biomedical Infrastructure 2013

Part of the book series: Studies in Computational Intelligence ((DSCC,volume 477))

Abstract

Health-related information, much of it consisting of images, is being predominantly accessed online by diverse groups of users ranging from medical professionals and researchers to students and the general public. This paper argues that broad-usage medical image retrieval is best approached as a sub-domain of generic image search. We discuss how search over a diverse corpus of biomedical and healthcare related images can benefit from a modern content-based image retrieval (CBIR) system based upon general photographic content classification techniques. The system features a flexible query language based upon a generic image concept ontology which can utilise both metadata (where available) and automatically extracted image content descriptors. Furthermore, the system supports both text-based querying as well as similarity-based searching and is thus well suited to iterative refinement of initial search results without the need for specialist knowledge of relevant keywords.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal, P., Sardana, H.K.: Enhancements in medicine by integrating content based image retrieval in computer-aided diagnosis. In: Second International Conference on Digital Image Processing, pp. 75461X–75461X. International Society for Optics and Photonics (2010)

    Google Scholar 

  2. Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: current status and future directions. Journal of Digital Imaging 24(2), 208–222 (2011)

    Article  Google Scholar 

  3. Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., Zezula, P.: Building a web-scale image similarity search system. Multimedia Tools and Applications 47(3), 599–629 (2010)

    Article  Google Scholar 

  4. Britton, D., Cass, A.J., Clarke, P.E.L., Coles, J., Colling, D.J., Doyle, A.T., Geddes, N.I., Gordon, J.C., Jones, R.W.L., Kelsey, D.P., et al.: Gridpp: the uk grid for particle physics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367(1897), 2447–2457 (2009)

    Article  Google Scholar 

  5. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) 40(2), 5 (2008)

    Article  Google Scholar 

  6. Deserno, T.M., Güld, M.O., Plodowski, B., Spitzer, K., Wein, B.B., Schubert, H., Ney, H., Seidl, T.: Extended query refinement for medical image retrieval. Journal of Digital Imaging 21(3), 280–289 (2008)

    Article  Google Scholar 

  7. Hanbury, A.: Medical information retrieval: an instance of domain-specific search. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1191–1192. ACM (2012)

    Google Scholar 

  8. Iakovidis, D.K., Pelekis, N., Kotsifakos, E.E., Kopanakis, I., Karanikas, H., Theodoridis, Y.: A pattern similarity scheme for medical image retrieval. IEEE Transactions on Information Technology in Biomedicine 13(4), 442–450 (2009)

    Article  Google Scholar 

  9. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: 12th International Conference on Computer Vision, pp. 2130–2137. IEEE (2009)

    Google Scholar 

  10. Long, L.R., Antani, S., Deserno, T.M., Thoma, G.R.: Content-based image retrieval in medicine: retrospective assessment, state of the art, and future directions. International Journal of Healthcare Information Systems 4(1), 1 (2009)

    Article  Google Scholar 

  11. Müller, H., Deserno, T.M.: Content-based medical image retrieval. In: Biomedical Image Processing - Methods and Applications, pp. 471–494. Springer (2011)

    Google Scholar 

  12. Müller, H., Despont-Gros, C., Hersh, W., Jensen, J., Lovis, C., Geissbuhler, A.: Health care professionals’ image use and search behaviour. In: Proceedings of Medical Informatics Europe (MIE 2006), pp. 24–32 (2006)

    Google Scholar 

  13. Müller, H., Geissbuhler, A.: Medical multimedia retrieval 2.0. Methods of Information in Medicine 3(suppl. 1), 55–63 (2008)

    Google Scholar 

  14. Petrakis, E.G.M., Faloutsos, A.: Similarity searching in medical image databases. IEEE Transactions onKnowledge and Data Engineering 9(3), 435–447 (1997)

    Article  Google Scholar 

  15. Rahman, M.M., Antani, S.K., Thoma, G.R.: A classification-driven similarity matching framework for retrieval of biomedical images. In: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 147–154. ACM (2010)

    Google Scholar 

  16. Shapiro, L., Atmosukarto, I., Cho, H., Lin, H., Ruiz-Correa, S., Yuen, J.: Similarity-based retrieval for biomedical applications. In: Case-Based Reasoning on Images and Signals, pp. 355–387 (2008)

    Google Scholar 

  17. Torjmen, M., Pinel-Sauvagnat, K., Boughanem, M.: Methods for combining content-based and textual-based approaches in medical image retrieval. In: Evaluating Systems for Multilingual and Multimodal Information Access, pp. 691–695 (2009)

    Google Scholar 

  18. Town, C.: Ontological inference for image and video analysis. Machine Vision and Applications 17(2), 94–115 (2006)

    Article  Google Scholar 

  19. Town, C.: Ontology based image and video analysisl. In: Computer Vision, Nova, pp. 303–328 (2011)

    Google Scholar 

  20. Town, C., Harrison, K.: Large-scale grid computing for content-based image retrieval. In: Aslib Proceedings, vol. 62(4/5), pp. 438–446. Emerald Group Publishing Limited (2010)

    Google Scholar 

  21. Town, C., Sinclair, D.: Language-based querying of image collections on the basis of an extensible ontology. International Journal of Image and Vision Computing 22(3), 251–267 (2004)

    Article  Google Scholar 

  22. Tsikrika, T., Müller, H., Kahn Jr., C.E.: Log analysis to understand medical professionals’ image searching behaviour. In: Proceedings of the 24th European Medical Informatics Conference (MIE 2012) (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK

    Christopher Town

Authors
  1. Christopher Town
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher Town .

Editor information

Editors and Affiliations

  1. , Curtin Sarawak Research Institute, Curtin University, Miri, 98009, Malaysia

    Amandeep S. Sidhu

  2. , Institute of Biological Sciences, University of Malaya, Kuala Lumpur, 50603, Malaysia

    Sarinder K Dhillon

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Town, C. (2013). Content-Based and Similarity-Based Querying for Broad-Usage Medical Image Retrieval. In: Sidhu, A., Dhillon, S. (eds) Advances in Biomedical Infrastructure 2013. Studies in Computational Intelligence, vol 477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37137-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37137-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37136-3

  • Online ISBN: 978-3-642-37137-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation