Regularized Restoration of Scintigraphic Images in Bayesian Frameworks

Scintigraphic imagery is widely used in nuclear medicine and in industrial testing. However, the image quality is very poor due to several degradations: Poisson noise, scattering of gamma photons, and non-stationary impulse response of the gamma detector. The restoration of scintigraphic images is typically an ill-posed inverse problem. In this paper, we propose a restoration method based on the Bayes-Markov approach. The regularization of such a problem is carried out by a Markovian prior. The discontinuity recovery and the restoration of the homogenous areas are improved thanks to the Markov random field (MRF) with an implicit line process. The performance of this approach is shown through the quality measures in terms of contrast around the edges and uniformity in the images, in comparison with two other existing methods.