We propose a variational approach for simultaneous reconstruction and multiclass segmentation of X-ray CT images, with limited field of view and missing data. We propose a simple energy minimisation approach, loosely based on a Bayesian rationale. The resulting non convex problem is solved by alternating reconstruction steps using an iterated relaxed proximal gradient, and a proximal approach for the segmentation. Preliminary results on synthetic data demonstrate the potential of the approach for synchrotron imaging applications.
CITATION STYLE
Lauze, F., Quéau, Y., & Plenge, E. (2017). Simultaneous reconstruction and segmentation of CT scans with shadowed data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10302 LNCS, pp. 308–319). Springer Verlag. https://doi.org/10.1007/978-3-319-58771-4_25
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