Skip to main content

Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7723)

Abstract

The effective communication between user and systems is one main aim in the Multimedia Information Retrieval field. In this paper the modality classification of images is used to expand the user queries within the ImageCLEF Medical Retrieval collection provided by organizers. Our main contribution is to show how and when results can be improved by understanding modality-related challenges. To do so, a detailed analysis of the results of the experiments carried out is presented and the comparison between these results shows that the improvement using modality class query expansion is query-dependent.

Keywords

  • Information Retrieval
  • Text-based Retrieval
  • Content-Based Image Retrieval
  • Merge Results Lists
  • Fusion
  • Indexing

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   49.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ayala, G., Domingo, J.: Spatial Size Distributions. Applications to Shape and Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1430–1442 (2011)

    CrossRef  Google Scholar 

  2. Bedrick, S., Kalpathy-Cramer, J.: Improving Retrieval Using External Annotations: OHSU at ImageCLEF 2010. In: Working Notes for the CLEF 2010 (2010)

    Google Scholar 

  3. Benavent, J., Benavent, X., de Ves, E., Granados, R., García-Serrano, A.: Experiences at ImageCLEF 2010 using CBIR and TBIR mixing information approaches. In: Working Notes for the CLEF 2010 (2010)

    Google Scholar 

  4. Castellanos, A., Benavent, X., Benavent, J., García-Serrano, A.: UNED-UV at Medical Retrieval Task of ImageCLEF 2011. In: Working Notes of the CLEF 2011 (2011)

    Google Scholar 

  5. Chevallet, J., Lim, J.: Using Ontology Dimensions and Negative Expansion to solve Precise Queries in CLEF Medical Task. In: Working Notes of the CLEF 2005 (2005)

    Google Scholar 

  6. Clinchant, S., Csurka, G., Ah-Pine, J., Jacquet, G., Perronin, F., Sánchez, J., Minoukadeh, K.: XRCE’s Participation in Wikipedia Retrieval, Medical Image Modality Classification Ad-hoc Retrieval Tasks of ImageCLEF 2010. In: Working Notes of the CLEF 2010 (2010)

    Google Scholar 

  7. Granados, R., Benavent, J., Benavent, X., de Ves, E., García-Serrano, A.: Multimodal information approaches for the Wikipedia collection at ImageCLEF 2011. In: Working Notes of the CLEF 2011 (2011)

    Google Scholar 

  8. Kalpathy-Cramer, J., Müller, H., Bedrick, S., Eggel, I.: Garcia Seco de Herrera, A., Tsikrika, T.: The CLEF 2011 medical image retrieval and classification tasks. In: Working Notes of the CLEF 2011 (2011)

    Google Scholar 

  9. Leon, T., Zuccarello, P., Ayala, G., de Ves, E., Domingo, J.: Applying logistic regression to relevance feedback in image retrieval systems. Pattern Recognition 40, 2621–2632 (2007)

    CrossRef  MATH  Google Scholar 

  10. Müller, H.: Garcia Seco de Herrera, A., Kalpathy-Cramer, J., Fushman, D., Antani, S., Eggel, I.: Overview of the ImageCLEF 2012 medical image retrieval and classification tasks. In: Working Notes of the CLEF 2012 (2012)

    Google Scholar 

  11. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2003)

    MATH  Google Scholar 

  12. Tirilly, P., Lu, K., Mu, X., Zhao, T., Cao, Y.: On modality classification and its use in text-based image retrieval in medical databases. In: 9th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 109–114 (2011)

    Google Scholar 

  13. Torjmen, M., Pinel-Sauvagnat, K., Boughanem, M.: Methods for Combining Content-Based and Textual-Based Approaches in Medical Image Retrieval. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 691–695. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  14. Villena-Román, J., Lana-Serrano, S., González-Cristóbal, J.-C.: MIRACLE at ImageCLEFmed 2007: Merging Textual and Visual Strategies to Improve Medical Image Retrieval. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 593–596. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Castellanos, A., Benavent, X., García-Serrano, A., Cigarrán, J. (2013). Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2012. Lecture Notes in Computer Science, vol 7723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36678-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36678-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36677-2

  • Online ISBN: 978-3-642-36678-9

  • eBook Packages: Computer ScienceComputer Science (R0)