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In this talk, I will introduce a new algorithm for Computed Tomography that simultaneously computes a reconstruction and a corresponding segmentation. Our algorithm uses learned dictionaries for both the reconstruction and the segmentation, constructed via discriminative dictionary learning using a set of corresponding images and segmentations. Numerical simulations demonstrate that our method provides better results than the other simultaneous reconstruction and segmentation methods or dictionary-based methods, especially when there are not sufficient projections.
This presentation is part of Minisymposium “MS43 - Variational Image Segmentation: Methods and Applications”
organized by: Jack Spencer (University of Liverpool) .