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

Camille Chapdelaine, Ali Mohammad-Djafari, Nicolas Gac, Estelle Parra


Published in:Fully3D 2017 Proceedings


3D computed tomography, non-destructive testing, iterative reconstruction algorithm, joint reconstruction and segmentation, Gauss-Markov-Potts
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-of-art methods. We also propose reconstruction quality indicators without reference which uses both reconstruction and segmentation returned by the algorithm.
Camille Chapdelaine
Laboratoire des signaux et systèmes, CNRS, Centralesupélec-Univ Paris Saclay, Gif-sur-Yvette/SAFRAN SA, Safran Tech, Pôle Technologie du Signal et de l'Information, Magny-Les-Hameaux, France
Ali Mohammad-Djafari
Laboratoire des signaux et systèmes, CNRS, Centralesupélec-Univ Paris Saclay, Gif-sur-Yvette, France
Nicolas Gac
Laboratoire des signaux et systèmes, CNRS, Centralesupélec-Univ Paris Saclay, Gif-sur-Yvette, France
Estelle Parra
SAFRAN SA, Safran Tech, Pôle Technologie du Signal et de l’Information, Magny-Les-Hameaux, France
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