Joint CT Reconstruction and Segmentation with Discriminative Dictionary Learning

Yiqiu Dong*, Per Christian Hansen, Hans Martin Kjer

*Corresponding author for this work

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Abstract

We present a novel algorithm for Computed Tomography (CT) 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. We give a detailed description of the implementation of our algorithm, and computer simulations demonstrate that our method provides better results than the other SRS or dictionary-based methods, especially when there are not sufficient projections. Moreover, due to the regularization, the segmentations from our method has more smooth class interfaces.
Original languageEnglish
JournalIeee Transactions on Computational Imaging
Volume4
Issue number4
Pages (from-to)528 - 536
ISSN2334-0118
DOIs
Publication statusPublished - 2018

Keywords

  • Tomographic reconstruction
  • Segmentation
  • Regularization
  • Learned dictionaries
  • Numerical optimization

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