Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data

  • François LauzeEmail author
  • Yvain Quéau
  • Esben Plenge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)


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.


Filter Back Projection Algebraic Reconstruction Technique Discrete Image Inverse Radon Filter Back Projection Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



F. Lauze acknowledges funding from the Innovation Fund Denmark and Mærsk Oil and Gas A/S, for the P\(^3\) Project. E. Plenge acknowledges funding from EUs 7FP under the Marie Skodowska-Curie grant (agreement no 600207), and from the Danish Council for Independent Research (grant ID DFF5054-00218).


  1. 1.
    Batenburg, K.J., Sijbers, J.: DART: a practical reconstruction algorithm for discrete tomography. IEEE Trans. Image Process. 20(9), 2542–2553 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE TIP 18(11), 2419–2434 (2009)MathSciNetGoogle Scholar
  3. 3.
    Bertsekas, D.: Incremental proximal methods for large scale convex optimization. Math. Program. Ser. B 129, 163–195 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Buzug, T.: Computed Tomography: From Photon Statistics to Modern Cone Beam CT. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Frickel, J., Quinto, E.T.: Characterization and reduction of artifacts in limited angle tomography. Inverse Probl. 29(12) (2013)Google Scholar
  6. 6.
    Andersen, M.S., Hansen, P.C.: Generalized row action methods for tomographic imaging. Numer. Algorithms 67, 121–144 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Natterer, F.: The Mathematics of Computerized Tomography, vol. 32. SIAM, Philadelphia (1986)zbMATHGoogle Scholar
  8. 8.
    Ramlau, R., Ring, W.: A Mumford-Shah approach for contour tomography. J. Comput. Phys. 221(2), 539–557 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Romanov, M., Dahl, A.B., Dong, Y., Hansen, P.C.: Simultaneous tomographic reconstruction and segmentation with class priors. Inverse Probl. Sci. Eng. 24(8) (2016)Google Scholar
  10. 10.
    van de Sompel, D., Brady, M.: Simultaneous reconstruction and segmentation algorithm for positron emission tomography and transmission tomography. In: Proceedings of the 2008 International Symposium on Biomedical Imaging (2008)Google Scholar
  11. 11.
    Storah, M., Weinmann, A., Frickel, J., Unser, M.: Joint image reconstruction and segmentation using the Potts model. Inverse Probl. 2(32) (2015)Google Scholar
  12. 12.
    Yoon, S., Pineda, A.R., Fahrig, R.: Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography. Med. Phys. 37(5), 2329–2340 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniverstity of CopenhagenCopenhagenDenmark
  2. 2.Computer Vision GroupTU MünchenMunichGermany

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