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User-Friendly Simultaneous Tomographic Reconstruction and Segmentation with Class Priors

  • Hans Martin KjerEmail author
  • Yiqiu Dong
  • Per Christian Hansen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

Simultaneous Reconstruction and Segmentation (SRS) strategies for computed tomography (CT) present a way to combine the two tasks, which in many applications traditionally are performed as two successive and separate steps. A combined model has a potentially positive effect by allowing the two tasks to influence one another, at the expense of a more complicated algorithm. The combined model increases in complexity due to additional parameters and settings requiring tuning, thus complicating the practical usability. This paper takes it outset in a recently published variational algorithm for SRS. We propose a simplification that reduces the number of required parameters, and we perform numerical experiments investigating the effect and the conditions under which this approach is feasible.

Keywords

Attenuation Coefficient Filter Back Projection Baseline Method Reconstruction Model Reconstruction Problem 
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.

Notes

Acknowledgments

This work is supported by Advanced grant no. 291405 “High-Definition Tomography” from the European Research Council.

References

  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.
    Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)zbMATHGoogle Scholar
  3. 3.
    Buzug, T.M.: Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Springer, Berline (2008)Google Scholar
  4. 4.
    Byrd, R.H., Hribar, M.E., Nocedal, J.: An interior point algorithm for large-scale nonlinear programming. SIAM J. Optim. 9(4), 877–900 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Depypere, M., Nuyts, J., Laperre, K., Carmeliet, G., Maes, F., Suetens, P.: The minimal entropy prior for simultaneous reconstruction and segmentation of in vivo microCT trabecular bone images. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 586–589. IEEE (2009)Google Scholar
  6. 6.
    Hansen, P.C., Saxild-Hansen, M.: AIR Tools - a MATLAB package of algebraic iterative reconstruction methods. J. Comput. Appl. Math. 236(8), 2167–2178 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Ramlau, R., Ring, W.: A Mumford-Shah level-set approach for the inversion and segmentation of X-ray tomography data. J. Comput. Phys. 221(2), 539–557 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Romanov, M., Dahl, A.B., Dong, Y., Hansen, P.C.: Simultaneous tomographic reconstruction and segmentation with class priors. Inverse Prob. Sci. Eng. 24(8), 1432–1453 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Van de Sompel, D., Brady, M.: Simultaneous reconstruction and segmentation algorithm for positron emission tomography and transmission tomography. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1035–1038. IEEE (2008)Google Scholar
  10. 10.
    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

  • Hans Martin Kjer
    • 1
    Email author
  • Yiqiu Dong
    • 1
  • Per Christian Hansen
    • 1
  1. 1.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark

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