We have developed a Bayesian method for image processing that uses the Gibbs random field model to incorporate a priori information for the purpose of improving the image quality. The types of prior information incorporated include the property of local continuity (i.e., neighboring pixels within a homogeneous region are similar), the limited spatial resolution of the imaging system, and possibly, some prior knowledge derived from corresponding images acquired by other modalities. We use the concept of `line sites' to separate regions that exhibit distinctly different tissue characteristics. A smoothing scheme is applied to each homogeneous region using a Gibbs distribution function. An efficient computational technique called iterative conditional average (ICA) method, which calculates the conditional mean values for each pixel and line site iteratively until convergence, is employed to compute the point estimates of the images. We have used this Bayesian approach to process images in nuclear medicine, digital radiography, and magnetic resonance imaging (MRI). In the processed images, we observed improvements in the spatial resolution, image contrast, and reduction in noise level.

Date Published: 1 June 1992
PDF: 3 pages
Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); doi: 10.1117/12.59458

Published in SPIE Proceedings Vol. 1652:
Medical Imaging VI: Image Processing
Murray H. Loew, Editor(s)