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

International audienceComputed Tomography is a powerful tool to reconstructa volume in 3D and has a wide field of applications inindustry for non-destructive testing. In these applications, the reconstructionprocess has a key importance to retrieve volumes thatcan be easily analyzed during the control. In this paper, in order toimprove the reconstruction quality, we present a Gauss-Markov-Potts prior model for the object to reconstruct in a Bayesianframework. This model leads to a joint reconstruction and segmentationalgorithm which is briefly described. The core of thepaper is the application of the algorithm on real 3D data. Weshow that our method obtains better results than other state-ofartmethods. We also propose reconstruction quality indicatorswithout reference which uses both reconstruction and segmentationreturned by the algorithm