Advertisement

Integration of neural networks and rule based systems in the interpretation of liver biopsy images

  • Nadia Bianchi
  • Claudia Diamantini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)

Abstract

Treatment of natural images requires, due to their complexity, to exploit high level knowledge, such as domain knowledge and heuristics, which are typically well formalized by rule based systems. However, the intrinsic variability and irregularity of objects in the image makes their characterization in terms of rules often unfeasible. Such variability and irregularity are, on the other hand, the ultimate reason for the existence of statistical methods. For these reasons, a hybrid system, exploiting characteristics of both approaches, may show better performances than purely syntactical or statistical systems in the interpretation of natural images. In this paper we present a hybrid system for image interpretation that integrates a rule based system with a Labeled Learning Vector Quantizer. The rule based system controls the interpretation process, by dynamically determining the interpretation strategy, and the Labeled Learning Vector Quantizer is exploited as classification kernel. The system has been tested on images of liver biopsies. Results on nuclei classification are here discussed.

Index terms

Hybrid Systems Rewriting Systems Pattern Recognition Adaptive Labeled Vector Quantization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    C.G. deBessonet, A many-valued approach to deduction and reasoning for artificial intelligence, Kluwer, Boston, 1991.Google Scholar
  2. [2]
    P. Bottoni, P. Mussio, and M. Protti, “Metareasoning in the determination of image interpretation strategies”, Patt. Rec. Lett. vol. 15, pp. 177–190, Feb. 1994.CrossRefGoogle Scholar
  3. [3]
    U. Cugini, P. Ferri, P. Mussio, M. Protti, “Pattern-directed restoration and vectorization of digitized engineering drawings”, Comp. & Graph., vol. 8, pp.337–350, 1984.Google Scholar
  4. [4]
    C. Diamantini and A. Spalvieri, “Vector quantization for minimum error probability,” International Conference on Artificial Neural Networks, vol. 2, pp. 1091–1094, Sorrento, IT, May 1994.Google Scholar
  5. [5]
    C. Diamantini and A. Spalvieri, “Quantizing for Minimum Bayes Risk,” Proc. International Symposium on Information Theory and its Applications, Sidney, Australia, Nov. 1994.Google Scholar
  6. [6]
    N.Dioguardi, “The liver as a self-organizing system”, Res. Clin. Lab., vol. 19, pp. 281–326, 1989Google Scholar
  7. [7]
    K. Fukunaga, “Introduction to Statistical Pattern Recognition,” New York, Acc. Press 1972.Google Scholar
  8. [8]
    S. Garibba, E. Guagnini and P. Mussio, “Multiple-valued Logic Tree: Meaning and Prime Implicants”, IEEE Trans. on Reliability, R vol. 34,5, Dec. 1985.Google Scholar
  9. [9]
    A.Gersho and R. M. Gray “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, 1992.Google Scholar
  10. [10]
    T.Kohonen, G.Barna and R.Chrisley, “Statistical pattern recognition with neural networks: benchmarking studies,” Proc. of the IEEE International Conference on Neural Networks, San Diego, CA, vol.1, pp.61–68, July 1988.Google Scholar
  11. [11]
    T.Kohonen, “The self organizing map,” Proc. of the IEEE, vol. 78, n. 9, pp. 1464–1480, Sept. 1990.CrossRefGoogle Scholar
  12. [12]
    P. Maes, “Computational reflection”, Know. Eng. Rev., vol.3,1, pp.1–19, 1988.Google Scholar
  13. [13]
    P. Mussio, M. Pietrogrande, P. Bottoni, M. Dell'Oca, E. Arosio, E. Sartirana, M.R. Finanzon, N. Dioguardi, “Automatic cell count in digital images of liver tissue sections”, Proc. 4th IEEE Symposium on Computer-based Medical Systems, pp. 153–160, IEEE Computer Society Press, 1991Google Scholar
  14. [14]
    M.Nagao, “Control strategies in pattern analysis”, Pattern Recognition, vol. 17, n. 1, pp. 45–56, 1984CrossRefGoogle Scholar
  15. [15]
    N. Rescher, Many-valued logic, McGraw-Hill, 1969.Google Scholar
  16. [16]
    A.Rosenfeld, “Computer Vision: Basic Principles” Proc. of the IEEE, vol. 76, n. 8, pp. 863–868, August 1988CrossRefGoogle Scholar
  17. [17]
    P. Smyth, R.M.Goodman and C.Higgins, “A Hybrid Rule-based/Bayesian Classifier,” Proc. of the 9th European Conference on Artificial Intelligence, pp. 610–615, Stokholm, Sverige, August 1990.Google Scholar
  18. [18]
    L. Tondl, “Problems of semantics”, Reitel, 1981.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Nadia Bianchi
    • 1
  • Claudia Diamantini
    • 2
  1. 1.Dipartimento di FisicaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Istituto di InformaticaUniversità degli Studi di AnconaAnconaItaly

Personalised recommendations