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Proceedings Paper

Statistical Models Of A Priori Information For Image Processing; II. Finite Distribution Range Constraints
Author(s): Z. Liang; R. Jaszczak
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Paper Abstract

A probabilistic formulation of statistical models of a priori source distribution information is presented with considerations of source strength correlations and finite distribution range constraints. A Bayesian analysis incorporating the a priori source distribution probabilistic information is given in treating measured data obeying Poisson statistics. A system of equations for determining the source distribution given the measured data is obtained by maximizing the a posteriori probability. An iterative approach for the solution is carried out by a Bayesian image processing algorithm derived using an expectation maximization technique. The iterative Bayesian algorithm is tested using computer generated ideal and experimental radioisotope phantom imaging noisy data. Improved results are obtained with the Bayesian algorithm over those of a maximum likelihood algorithm. A quantitative measurement of the improvement is obtained by employing filtered objective criteria functions.

Paper Details

Date Published: 16 December 1988
PDF: 6 pages
Proc. SPIE 0974, Applications of Digital Image Processing XI, (16 December 1988); doi: 10.1117/12.948433
Show Author Affiliations
Z. Liang, Duke University Medical Center (United States)
R. Jaszczak, Duke University Medical Center (United States)

Published in SPIE Proceedings Vol. 0974:
Applications of Digital Image Processing XI
Andrew G. Tescher, Editor(s)

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