Fault-Tolerant Medical Image Interpretation

  • A. Sood
  • H. Wechsler
Conference paper
Part of the NATO ASI Series book series (volume 98)


Novel medical imaging techniques have been introduced over the last decade. They provide for the non-invasive study of internal structures and their dynamic behavior because of the large bandwidth characteristics of the new sensors. The processing and automatic interpretation of such images, however, lags far behind. We suggest herein a synergetic approach where novel techniques derived from artificial intelligence (AI), computer vision (CV) and neural networks (NN) could be integrated towards robust and automatic image interpretation. Such image interpretation would be relevant for the analysis of internal organs and/or tissue and to the understanding of time-varying (dynamic) imagery. Within the medical area it is important that such analysis be fault-tolerant (low sensitivity) to noisy data, occlusion/overlap, geometric distortions, and still be efficient.


Associative Memory Markov Random Fields Scale Space Range Image Response Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Acharya, R., Hefferman, P.B., Robb, R., and Wechsler, H. (1987), High-Speed 3-D Imaging of the beating heart using temporal estimation, Computer Vision, Graphics, and Image Processing, 39, 279–290.CrossRefGoogle Scholar
  2. Geman, S., and Geman, D. (1984), Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images, IEEE Trans. on PAMI, Vol. 6, No. 6, 721–741.CrossRefGoogle Scholar
  3. Rosenfeld, A. (Ed.) (1984), Multiresolution Image Processing and Analysis, Springer-Verlag.Google Scholar
  4. Pizer, S. (1989), Multiscale methods and the segmentation of medical images, this volume.Google Scholar
  5. Uhr, L. (1988), Parallel Computer Vision, Academic Press.Google Scholar
  6. Jacobson, L., and Wechsler, H. (1988), Joint spatial/spatial-frequency representations, Signal Processing, Vol. 14, No. 1, 95–102.CrossRefGoogle Scholar
  7. Shah, M., Sood, A. and Jain, R. (1986), Pulse and staircase edge models, Computer Vision, Graphics, and Image Processing, Vol. 34, 321–343.CrossRefGoogle Scholar
  8. Sood, A. and Shah, M. (1987), Scale space technique to finding primitives in images with application to road following, Proceedings SPIE—Applications of Artificial Intelligence V.Google Scholar
  9. Witkin, A.P. (1983), Scale space filtering, Proceedings of IJCAI.Google Scholar
  10. Yuille, A.L. and Poggio, T. (1983), Fingerprints theorems for zero crossings, MIT AI memo 730.Google Scholar
  11. Besl, P. and Jain, R. (1988), Segmentation through variable-order surface fitting, IEEE Trans. on PAMI, Vol. 10, No. 2, 167–192.CrossRefGoogle Scholar
  12. Al-Hujaze, E. and Sood, A. (1988), Range data description based on multiple characteristics, Proceedings 1988 Goddard Conference on Space Applications of Artificial Intelligence.Google Scholar
  13. Wechsler, H., and Zimmerman, L. (1988), 2-D Invariant object recognition using DAM, IEEE Trans. on PAMI, Vol. 10, 6 (in print).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • A. Sood
    • 1
  • H. Wechsler
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

Personalised recommendations