SPATIALLY VARYING BAYESIAN IMAGE ESTIMATION

Rationale and Objectives. Second-order neighborhoods and a spatially v arying prior were incorporated into Bayesian image estimation (BIE) to improve image contrast-to-noise ratios (CNRs) while preserving image resolution. Methods. Second-order neighborhoods were incorporated into the BIE algorithm. A spatially varying BIE (SVBIE) algorithm was deve loped by incorporating a spatially varying prior. The two algorithms w ere used to process an anthropomorphic chest phantom image, CNRs, reso lution, and image appearance were evaluated.Results. The use of second -order neighborhoods alone improved the CNR in the mediastinum and deg raded the resolution. SVBIE demonstrated no degradation of resolution. In the lung region, SVBIE enhanced the CNR but did not perform as wel l as BIE, In the mediastinum, the SVBIE technique outperformed the old er technique and provided a dramatic increase in the CNR over the orig inal image. Conclusion. The SVBIE technique provides improved image CN R with no loss of resolution.