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Binary Tomography Reconstruction with Stochastic Diffusion Based on Level-set and Total Variation Regularization

B. Sixou, L. Wang, S. Rit, F. Peyrin


In this work, we address the problem of the reconstruction of binary images from a small number of noisy tomographic projections. Recently, a new stochastic level-set approach was investigated to refine the reconstruction. The main limitation of this method is that it is only changing the boundaries of the reconstructed regions. In this work, we study a new stochastic approach based on Total Variation (TV) regularization with box constraints. The main advantage of this method is that random shape and boundaries variations can be included in a new way and that topology changes can be also added. The methods are tested on two complex bone micro-CT cross-section images for different noise levels and number of projections. While for the higher noise levels, the best reconstructions are obtained with a stochastic diffusion based on the Total Variation regularization, large decreases of the reconstruction errors are obtained when shape and topology noises are
used simultaneously.


X-ray imaging, discrete tomography, level-set regularization, total variation regularization, inverse problems.

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