Journal article
Simultaneous tomographic reconstruction and segmentation with class priors
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
We consider tomographic imaging problems where the goal is to obtain both a reconstructed image and a corresponding segmentation. A classical approach is to first reconstruct and then segment the image; more recent approaches use a discrete tomography approach where reconstruction and segmentation are combined to produce a reconstruction that is identical to the segmentation.
We consider instead a hybrid approach that simultaneously produces both a reconstructed image and segmentation. We incorporate priors about the desired classes of the segmentation through a Hidden Markov Measure Field Model, and we impose a regularization term for the spatial variation of the classes across neighbouring pixels.
We also present an efficient implementation of our algorithm based on state-of-the-art numerical optimization algorithms. Simulation experiments with artificial and real data demonstrate that our combined approach can produce better results than the classical two-step approach.
Language: | English |
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Publisher: | Informa UK Limited |
Year: | 2015 |
Pages: | 1432-1453 |
ISSN: | 17415985 , 17415977 and 10267549 |
Types: | Journal article |
DOI: | 10.1080/17415977.2015.1124428 |
ORCIDs: | Dahl, Anders Bjorholm , Dong, Yiqiu and Hansen, Per Christian |