X-ray computed tomography simultaneous image reconstruction and contour detection using a hierarchical markovian model

Abstract : The 3D X-ray Computed Tomography (CT) is used in many domains. In medical imaging and industrial Non Destructive Testing (NDT) applications, this technique becomes of great interest. In these applications, very often, we need not only to reconstruct the image, but also to detect the contours between the homogeneous regions of the piecewise continuous image. Generally, contours are obtained by a post processing from the reconstructed image. In this paper, we propose a method to estimate image and contour simultaneously. For this we use the Bayesian approach with a prior model in which the relationship between the image and its contour is considered by using a hierarchical Markovian model, and use a sparsity enforcing prior model for the contours. This proposed method can be used for reconstructions when the image is piecewise continuous. The simulation results are compared with some state of the art methods, and they show the efficiency of simultaneously reconstructing and edge detecting by using proposed method.
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Li Wang, Ali Mohammad-Djafari, Nicolas Gac. X-ray computed tomography simultaneous image reconstruction and contour detection using a hierarchical markovian model. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. pp.6070-6074, ⟨10.1109/ICASSP.2017.7953322⟩. ⟨hal-01490508⟩

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