Skip to main content

Integration of Strategies Based on Relevance Feedback into a Tool for the Retrieval of Mammographic Images

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a high percentage of these cases can be 100% healed if they are detected in early stages. Because symptoms are not visible as far as advanced stages, it makes the treatments more aggressive and also less efficient. Therefore, it is necessary to develop new strategies to detect the formation in early stages.

We have developed a tool based on a Case-Based Reasoning kernel for retrieving mammographic images by content analysis. One of the main difficulties is the introduction of knowledge and abstract concepts from domain into the retrieval process. For this reason, the article proposes integrate the human experts perceptions into it by means of an interaction between human and system using a Relevance Feedback strategy. Furthermore, the strategy uses a Self-Organization Map to cluster the memory and improve the time interaction.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Aamodt, A., Plaza, E.: Case-based reasoning: Foundations issues, methodological variations, and system approaches. IA Communications 7, 39–59 (1994)

    Google Scholar 

  • Chen, Z., Wenyin, L., Hu, C., Li, M., Zhang, H.: ifind: a web image search engine. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p. 450. ACM Press, New York (2001)

    Chapter  Google Scholar 

  • Cox, I.J., Minka, T.P., Papathomas, T.V.: The bayesian image retrieval system, pichunter: Theory, implementation, and psychophysical experiments. IEEE Transaction on Image Processing – special issue on digital libraries 9, 20–37 (2000)

    Google Scholar 

  • Fornells, A., Golobardes, E., Vernet, D., Corral, G.: Unsupervised case memory organization: Analysing computational time and soft computing capabilities. In: 8th European Conference on Case-Based Reasoning (2006) (in press)

    Google Scholar 

  • Golobardes, E., Llorá, X., Salamö, M., Martí, J.: Computer aided diagnosis with case-based reasoning and genetic algorithms. Journal of Knowledge Based Systems 15, 45–52 (2002)

    Article  Google Scholar 

  • Han, K., Myaeng, S.: Image organization and retrieval with automatically constructed feature vectors. In: SIGIR Forum, special issue, pp. 157–165 (1996)

    Google Scholar 

  • Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  • Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.J.: The digital database for screening mammography. In: Int. Workshop on Dig. Mammography (2000)

    Google Scholar 

  • Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: Websom: Self-organizing maps of document collections. Neurocomputing 21(1), 101–117 (1998)

    Article  MATH  Google Scholar 

  • Kohonen, T.: Self-Organization and Associative Memory. In: Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1984); 3rd edn. (1989)

    Google Scholar 

  • Laaksonen, J., Koskela, M., Oja, E.: Picsom: Self-organization maps for contentbased image retrieval. In: Proceedings of Int. Joint Conference on NN (1999)

    Google Scholar 

  • Martí, J., Español, J., Golobardes, E., Freixenet, J., García, R., Salamó, M.: Classification of microcalcifications in digital mammograms using case-based reasonig. In: International Workshop on Digital Mammography (2000)

    Google Scholar 

  • Müller, H., Müller, W., Marchand-Maillet, S., Pun, T.: Strategies for positive and negative relevance feedback in image retrieval. In: International Conference on Pattern Recognition, vol. 1, pp. 1043–1046 (2000)

    Google Scholar 

  • Oliver, A., Freixenet, J., Bosch, A., Raba, D., Zwiggelaar, R.: Automatic classification of breast tissue. In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 431–438 (2005)

    Google Scholar 

  • Oliver, A., Freixenet, J., Zwiggelaar, R.: Automatic classification of breast density. In: IEEE International Conference on Image Processing, vol. 2, pp. 1258–1261 (2005)

    Google Scholar 

  • Samuels, T.H.: Illustrated Breast Imaging Reporting and Data System BIRADS, 3rd edn. American College of Radiology Publications (1998)

    Google Scholar 

  • Sclaroff, S., Taycher, L., LaCascia, M.: Imagerover: A content-based image browser for the world wide web. Technical Report 5 (1997)

    Google Scholar 

  • Suckling, J., Parker, J., Dance, D.R.: The mammographic image analysis society digital mammogram database. In: Gale, A.G., et al. (eds.) Proceedings of 2nd International Workshop on Digital Mammography, pp. 211–221 (1994)

    Google Scholar 

  • Winfields, D., Silbiger, M., Brown, G.: Technology transfer in digital mamography. In: Report of the Joint National Cancer Institute, Workshop of May 19-20, Invest Radiololgy, pp. 507–515 (1994)

    Google Scholar 

  • Zhang, H., Zhong, D.: A scheme for visual feature based image indexing. In: Storage and Retrieval for Image and Video Databases III, vol. 2420th (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fornells, A., Golobardes, E., Vilasís, X., Martí, J. (2006). Integration of Strategies Based on Relevance Feedback into a Tool for the Retrieval of Mammographic Images. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_14

Download citation

  • DOI: https://doi.org/10.1007/11875581_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics