A joint segmentation and reconstruction algorithm for 3D Bayesian Computed Tomography using Gaus-Markov-Potts Prior Model

Abstract : Gauss-Markov-Potts models for images and its use in manyimage restoration, super-resolution and Computed Tomography(CT) have shown their effective use for Non DestructiveTesting (NDT) applications. In this paper, we propose a 3DGauss-Markov-Potts model for 3D CT for NDT applications.Thanks to this model, we are able to perform a joint reconstructionand segmentation of the object to control, which isvery useful in industrial applications. First, we describe ourprior models for each unknown of the problem. Then, wepresent results on simulated data and compare them to thoseof Total Variation (TV) minimization algorithm. Two qualityindicators exploiting the segmentation are also proposed.
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Submitted on : Friday, September 15, 2017 - 4:27:17 PM
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Camille Chapdelaine, Ali Mohammad-Djafari, Nicolas Gac, Estelle Parra-Denis. A joint segmentation and reconstruction algorithm for 3D Bayesian Computed Tomography using Gaus-Markov-Potts Prior Model. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. ⟨hal-01588443⟩

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