Statistical models of a priori information for image processing; II. Finite distribution range constraints

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.