Study and performance evaluation of statistical methods in image processing

Two statistical image processing formalisms involving the entropy concept and Bayesian analysis are studied. Iterative imaging algorithms of the formalisms are formulated by employing, for the purpose of performance evaluation and easy implementation, the steepet descent method for the solution of entropy concept and the expectation maximization technique for the solution of Bayesian analysis. Quantitative evaluation and comparison of the convergence performance of the iterative algorithms on computer generated ideal and experimental radioisotope phantom imaging noisy data are given. The study concludes that the entropy algorithm can converge relatively fast, but it is very sensitive to noise in measured data due to the ill-posed nature of inverse problems and its lack of ability to consider the statistics of data and has the advantage of Bayesian algorithm converges monotonically even with noisy data and has the advantage of considering both the a priori source distribution information and the statistical fluctuation of measured data.