Probabilistic Image Models and Their Information-Theoretic Properties

Ya-Qin Zhang; Murray H. Loew; Raymond L. Pickholtz

doi:10.1117/12.953247

25 May 1989 Probabilistic Image Models and Their Information-Theoretic Properties

Ya-Qin Zhang, Murray H. Loew, Raymond L. Pickholtz

Proceedings Volume 1092, Medical Imaging III: Image Processing; (1989) https://doi.org/10.1117/12.953247
Event: Medical Imaging III, 1989, Newport Beach, CA, United States

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.

Citation Download Citation

Ya-Qin Zhang, Murray H. Loew, and Raymond L. Pickholtz "Probabilistic Image Models and Their Information-Theoretic Properties", Proc. SPIE 1092, Medical Imaging III: Image Processing, (25 May 1989); https://doi.org/10.1117/12.953247

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