A spatial interaction model for statistical image processing.
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Biomedical subjects
R Jaszczak
Publications and source records attributed to R Jaszczak.
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 steepest 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 fluctuation; while the 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.
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Simultaneous reconstruction, segmentation, and edge enhancement of relatively piecewise continuous images with intensity-level information.
A multinomial image model is proposed which uses intensity-level information for reconstruction of contiguous image regions. The intensity-level information assumes that image intensities are relatively constant within contiguous regions over the image-pixel array and that intensity levels of these regions are determined either empirically or theoretically by information criteria. These conditions may be valid, for example, for cardiac blood-pool imaging, where the intensity levels (or radionuclide activities) of myocardium, blood-pool, and background regions are distinct and the activities within each region of muscle, blood, or background are relatively uniform. To test the model, a mathematical phantom over a 64 x 64 array was constructed. The phantom had three contiguous regions. Each region had a different intensity level. Measurements from the phantom were simulated using an emission-tomography geometry. Fifty projections were generated over 180 degrees, with 64 equally spaced parallel rays per projection. Projection data were randomized to contain Poisson noise. Image reconstructions were performed using an iterative maximum a posteriori probability procedure. The contiguous regions corresponding to the three intensity levels were automatically segmented. Simultaneously, the edges of the regions were sharpened. Noise in the reconstructed images was significantly suppressed. Convergence of the iterative procedure to the phantom was observed. Compared with maximum likelihood and filtered-backprojection approaches, the results obtained using the maximum a posteriori probability with the intensity-level information demonstrated qualitative and quantitative improvement in localizing the regions of varying intensities.
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