On the Relationship between Physical Metrics and Numerical Observer Studies for the Evaluation of Image Reconstruction Algorithms

  • G. T. Herman
  • K. T. D. Yeung
Conference paper
Part of the NATO ASI Series book series (volume 98)


Physical metrics, such as the root mean squared distance, are simple to calculate, but we do not have a great confidence in their medical relevance. We have a greater confidence in numerical observer studies, but they are more complicated to carry out. Suppose that a claim is made that a particular physical metric yields similar results to a particular numerical observer study. How can we evaluate the validity of such a claim? In this paper we introduce the notion of “rank ordering similarity” to measure the relationship (closeness, similarity) between algorithm performance measures. We use this notion to study the relationship between a particular physical metric (relative clipped error) and a particular numerical observer method (ROC on threshold). We also investigate the role of contrast in this context.


Rank Ordering Human Observer Physical Metrics Statistical Pattern Recognition Image Reconstruction Algorithm 
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. Fiete, R.D., Barrett, H.H., Smith, W.E., and Myers, K.J. (1987). Hotelling trace criterion and its correlation with human observer performance, Journal of the Optical Society of America A 4, pp. 945–953.CrossRefGoogle Scholar
  2. Fukunaga, K. (1972). Introduction to statistical pattern recognition, Academic Press, New York.Google Scholar
  3. Green, D.M., and Swets, J.A. (1966). Signal detection theory and psychophysics, John Wiley & Sons, New York.Google Scholar
  4. Hanson, K.M. (1988). Method to evaluate image-recovery algorithms based on task performance, Proceedings of the Society of Photo-Optical Instrumentation Engineers 914, pp. 336–342.Google Scholar
  5. Herman, G.T. (1980). Image reconstruction from projections: The fundamentals of computerized tomography, Academic Press, New York.Google Scholar
  6. Yeung, K.T.D., and Herman, G.T. (1989). Objective measures to evaluate the performance of reconstruction algorithms, Medical Imaging III: Proceedings of the SPIE 1092, to appear.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • G. T. Herman
    • 1
  • K. T. D. Yeung
    • 1
  1. 1.University of PennsylvaniaUSA

Personalised recommendations