Joint Reconstruction and Segmentation of Real 3D Data in Computed Tomography thanks to a Gauss-Markov-Potts Prior Model,

Computed Tomography is a powerful tool to reconstruct a volume in 3D and has a wide field of applications in industry for non-destructive testing. In these applications, the reconstruction process has a key importance to retrieve volumes that can be easily analyzed during the control. In this paper, in order to improve the reconstruction quality, we present a Gauss-Markov- Potts prior model for the object to reconstruct in a Bayesian framework. This model leads to a joint reconstruction and segmentation algorithm which is briefly described. The core of the paper is the application of the algorithm on real 3D data. We show that our method obtains better results than other state-ofart methods. We also propose reconstruction quality indicators without reference which uses both reconstruction and segmentation returned by the algorithm.