Skip to main content

A fuzzy knowledge-based system for biomedical image interpretation

  • 8. Uncertainty In Intelligent Systems
  • Conference paper
  • First Online:
  • 139 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 521))

Abstract

A general purpose knowledge-based system for biomedical image interpretation is presented. The system acquires knowledge directly from the experts by means of a user friendly dialogue. The knowledge introduced tailors the system to a particular biomedical application. Frame representation technique is used for the representation of descriptive knowledge and a fuzzy reasoning strategy, based on fuzzy production rules, is adopted to manipulate the certain and uncertain knowledge contained into Frame Slots and to deduce interpretations. A detailed description of the application of the system to the analysis of CT images of vertebrae for the quantity evaluation of the bone mineral content is provided.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adlassnig K., Kolarz G., Scheithauer W., 1985, Present State of the Medical Expert System Cadiag-2, Methods of Information in Medicine, F.K. Schattauer Verlag GmbH.

    Google Scholar 

  2. E. Binaghi, 1990, A fuzzy Logic Inference Model for a Rule-Based System in Medical Diagnosis, Expert Systems, Vol.7,No.3, 134–141.

    Google Scholar 

  3. Davis R., Buchanan B., Shortliffe E., 1977, Production Rules as a Representation for a Knowledge-Based Consultation Program, Artificial Intelligence, 8, 15–45.

    Article  Google Scholar 

  4. A. Della Ventura, G. Pennati, M. Sideri, 1989, Computer Aided Screening of Subjects at Risk for Cervical Neoplasia, in Recent Issues in Pattern Analysis and Recognition, Lecture Notes on Computer Science, Vol.399, 338–350, Springer-Verlag.

    Google Scholar 

  5. R. Fikes R. and T. Kehler T., 1985, The Role of Frame-Based Representation in Reasoning", Comm. of ACM, vol.28.

    Google Scholar 

  6. Kalender W., Klotz E., Suess C., 1987, Vertebral Bone Mineral Analysis: An Integrated Approach with CT, Radiology, Vol.164, 419–423.

    PubMed  Google Scholar 

  7. H. J. Levesque, R. J. Brachman, "A fundamental tradeoff in knowledge representation and reasoning", in R.J. Brachman, H.J. Levesque, Readings in knowledge representation, Morgan Kaufmann Publishers, pp. 42–70, 1985.

    Google Scholar 

  8. S. Pal, D. Dutta Majumder, 1986, Fuzzy Mathematical Approach to Pattern Recognition, Wiley Eastern Limited.

    Google Scholar 

  9. A. Rosenfeld, 1986, Dialog: Expert Vision Systems: Some Issues, Computer Vision, Graphics and Image Processing, Vol. 34, 99–117.

    Google Scholar 

  10. Zadeh L., 1965, Fuzzy Sets, Information and control, 8, 1965, 338–353.

    Article  Google Scholar 

  11. Zadeh L., 1981, PRUF — a meaning representation language for natural languages", in E. H. Mamdani and B.R. Gaines, Fuzzy Reasoning and its Applications, Academic Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Bernadette Bouchon-Meunier Ronald R. Yager Lotfi A. Zadeh

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Binaghi, E., Della Ventura, A., Rampini, A., Schettini, R. (1991). A fuzzy knowledge-based system for biomedical image interpretation. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028136

Download citation

  • DOI: https://doi.org/10.1007/BFb0028136

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54346-6

  • Online ISBN: 978-3-540-47580-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics