Spatially varying Bayesian image estimation.
OBJECTIVE
Second-order neighborhoods and a spatially varying 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 developed by incorporating a spatially varying prior. The two algorithms were used to process an anthropomorphic chest phantom image. CNRs, resolution, and image appearance were evaluated.


RESULTS
The use of second-order neighborhoods alone improved the CNR in the mediastinum and degraded the resolution. SVBIE demonstrated no degradation of resolution. In the lung region, SVBIE enhanced the CNR but did not perform as well as BIE. In the mediastinum, the SVBIE technique outperformed the older technique and provided a dramatic increase in the CNR over the original image.


CONCLUSIONS
The SVBIE technique provides improved image CNR with no loss of resolution.
Baydush
A H
AH
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
Floyd
C E
CE
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D011856:Radiographic Image Enhancement; D013902:Radiography, Thoracic; D012542:Scattering, Radiation