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Binary Tomography Reconstructions With Stochastic Level-Set Methods
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DOI：10.1109/LSP.2014.2375511.png)
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Abstract
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In this work, we propose a stochastic level-set method to reconstruct binary tomography cross-sections from few projections. A first reconstruction image is obtained with a level-set regularization method. The reconstruction is then refined with a stochastic partial differential equation based on a Stratanovitch formulation. The reconstruction results are compared with the ones obtained with the classical simulated annealing method. The methods are tested on a complex bone mu - CT cross-section for different noise levels and number of projections. The best reconstruction results are obtained with the stochastic level set-method.
Keywords:
Binary tomography
inverse problems
level-set
x-ray imaging
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