A fuzzy knowledge-based system for biomedical image interpretation

  • E. Binaghi
  • A. Della Ventura
  • A. Rampini
  • R. Schettini
8. Uncertainty In Intelligent Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 521)


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.


biomedical image interpretation knowledge-based systems frames fuzzy reasoning 


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  1. [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. [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. [3]
    Davis R., Buchanan B., Shortliffe E., 1977, Production Rules as a Representation for a Knowledge-Based Consultation Program, Artificial Intelligence, 8, 15–45.CrossRefGoogle Scholar
  4. [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. [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. [6]
    Kalender W., Klotz E., Suess C., 1987, Vertebral Bone Mineral Analysis: An Integrated Approach with CT, Radiology, Vol.164, 419–423.PubMedGoogle Scholar
  7. [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. [8]
    S. Pal, D. Dutta Majumder, 1986, Fuzzy Mathematical Approach to Pattern Recognition, Wiley Eastern Limited.Google Scholar
  9. [9]
    A. Rosenfeld, 1986, Dialog: Expert Vision Systems: Some Issues, Computer Vision, Graphics and Image Processing, Vol. 34, 99–117.Google Scholar
  10. [10]
    Zadeh L., 1965, Fuzzy Sets, Information and control, 8, 1965, 338–353.CrossRefGoogle Scholar
  11. [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

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • E. Binaghi
    • 1
  • A. Della Ventura
    • 1
  • A. Rampini
    • 1
  • R. Schettini
    • 1
  1. 1.Istituto di Fisica Cosmica e Tecnologie Relative - CNR - MilanoMilano

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