Abstract
In this article, we address the problem of fully three-dimensional (3D) binary image reconstruction from three projections. The regularization approach relies on the use of a 3D Ising model which is a particular Gibbs prior. The problem is then equivalent to the minimization of a nonconvex energy function on discrete values. After an evaluation of standard optimization methods such as simulated annealing or its deterministic version ICM, we propose a new algorithm called parallel randomized ICM. It improves the reconstruction quality compared to ICM solutions while keeping reasonable reconstruction time. Its performances are evaluated from simulated projections for different 3D test images. Its application to real data is presented. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 135–146, 1998
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