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Proceedings Paper

Probabilistic Image Models and Their Information-Theoretic Properties
Author(s): Ya-Qin Zhang; Murray H. Loew; Raymond L. Pickholtz
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Paper Abstract

In this paper, we propose a new method to construct the joint probability model from a specified first-order distribution and correlation structure. The construction procedure can be interpreted in two ways: (1) It embodies the maximum entropy principle, or (2) It is considered as a correlated non-Gaussian source generated by a nonlinear transformation from a correlated Gaussian source. Its stochastic properties [mean,correlation] and information-theoretic properties [entropy, rate-distortion bound] are examined. An example for the lognormal distribution is given to illustrate the construction process and the characteristics of the source. This approach should remove the limitations imposed by earlier methods, and make for more realistic modeling of medical images.

Paper Details

Date Published: 25 May 1989
PDF: 9 pages
Proc. SPIE 1092, Medical Imaging III: Image Processing, (25 May 1989); doi: 10.1117/12.953247
Show Author Affiliations
Ya-Qin Zhang, The George Washington University (United States)
Murray H. Loew, The George Washington University (United States)
Raymond L. Pickholtz, The George Washington University (United States)


Published in SPIE Proceedings Vol. 1092:
Medical Imaging III: Image Processing
Samuel J. Dwyer; R. Gilbert Jost; Roger H. Schneider, Editor(s)

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