A Joint Segmentation and Reconstruction Algorithm for 3d Bayesian Computed Tomography Using Gauss-markov-potts Prior Model

Gauss-Markov-Potts models for images and its use in many image restoration and super-resolution problems have shown their effective use for Non Destructive Testing (NDT) applications. In this paper, we propose a 3D Gauss-Markov-Potts model for 3D CT for NDT applications. Thanks to this model, we are able to perform a joint reconstruction and segmentation of the object to control, which is very useful in industrial applications. First, we describe our prior models for each unknown of the problem. Then, we present results on simulated data and compare them to those of Total Variation (TV) minimization algorithm. Two quality indicators exploiting the segmentation are also proposed.