Binary Tomography Reconstructions With Stochastic Level-Set Methods

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...

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Bibliographic Details
Published in:IEEE signal processing letters 2015-07, Vol.22 (7), p.920-924
Main Authors: Wang, L., Sixou, B., Peyrin, F.
Format: Article
Language:English
Subjects:
Binary tomography
Bones
Computed tomography
Computer Science
Cross sections
Engineering Sciences
Image reconstruction
inverse problems
level-set
Medical Imaging
Noise level
Noise levels
Projection
Reconstruction
Signal and Image processing
Simulated annealing
Stochasticity
Tomography
x-ray imaging
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Summary: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 μ- CT cross-section for different noise levels and number of projections. The best reconstruction results are obtained with the stochastic level set-method.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2375511