Joint Reconstruction and Segmentation of Real 3D Data in Computed Tomography thanks to a Gauss-Markov-Potts Prior Model,
Résumé
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.
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Fully3D2017Chapdelaine.pdf (820.7 Ko)
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Fully3D2017PosterChapdelaine.pdf (1.45 Mo)
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