DOI QR코드

DOI QR Code

Performance Comparison of Regularization Methods in Electrical Resistance Tomography

전기 저항 단층촬영법에서의 조정기법 성능비교

  • Kang, Suk-In (Faculty of Applied Energy System, Major of Electronic Engineering, Jeju National University) ;
  • Kim, Kyung-Youn (Dept. of Electronic Engineering, Jeju National University)
  • Received : 2016.07.13
  • Accepted : 2016.08.30
  • Published : 2016.09.30

Abstract

Electrical resistance tomography (ERT) is an imaging technique where the internal resistivity distribution inside an object is reconstructed. The ERT image reconstruction is a highly nonlinear ill-posed problem, so regularization methods are used to achieve desired image. The reconstruction outcome is dependent on the type of regularization method employed such as l2-norm, l1-norm, and total variation regularization method. That is, use of an appropriate regularization method considering the flow characteristics is necessary to attain good reconstruction performance. Therefore, in this paper, regularization methods are tested through numerical simulations with different flow conditions and the performance is compared.

전기 저항 단층촬영법(ERT)은 대상체 내부 단면의 저항률 분포를 추정하고 이를 영상화하는 기술이다. ERT의 영상복원은 매우 비정치성이 강한 역문제의 일종으로 의미있는 영상을 얻기 위해서는 조정기법이 사용된다. 대표적으로 l2-norm 조정기법, l1-norm 조정기법, Total Variation 조정기법이 사용되며, 조정기법에 따라 ERT의 영상복원 성능이 달라진다. 즉, 상황에 맞는 적절한 조정기법의 사용은 ERT 영상 복원을 개선할 수 있다. 따라서, 본 논문에서는 모의실험을 통하여 상황에 따른 세 가지 조정기법의 영상복원 성능을 비교하였다.

Keywords

References

  1. D. S. Holder, Electrical Impedance Tomography: Methods, History and Applications, IOP Publishing Ltd, 2005.
  2. M. Vauhkonen, Electrical Impedance Tomography and prior information, Ph.D. Thesis, University of Kuopio, Finland, 1997.
  3. K. Y. Kim and B. S. Kim, "Regularized Modified Newton-Raphson Algorithm for Electrical Impedance Tomography Based on the Exponentially Weighted Least Square Criterion," j.inst.Korean.electr.electron.eng, vol. 4, no. 2, pp. 77-84, 2000.
  4. S. I. Kang and K. Y. Kim, "Image Reconstruction Using Iterative Regularization Scheme Based on Residual Error in Electrical Impedance Tomography," j.inst.Korean.electr. electron.eng, vol. 18, no. 2, pp. 272-281, 2014.
  5. B. Jin, T. Khan and P. Maass, "A reconstruction algorithm for electrical impedance tomography based on sparsity regularization," Int J Numer Methods Eng, vol. 89, pp. 337-353, 2011.
  6. T. Dai and A. Adler, "Electrical Impedance Tomography Reconstruction Using l1 Norms for Data and Image Terms," 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, August. 2008, pp. 2721-2724.
  7. A. Borsic, B. M. Graham, A. Adler and W. R. B. Lionheart, "Total Variation Regularization in Electrical Impedance Tomography," Technical Report 92, School of Mathematics, University of Manchester, pp. 1-26, 2007.
  8. A. Borsic, B. M. Graham, A. Adler and W. R. B. Lionheart, "In vivo Impedance Imaging With Total Variation Regularization," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 44-54, 2010. https://doi.org/10.1109/TMI.2009.2022540
  9. K. S. Cheng, D. Isaacson, J. C. Newell and D. G. Gisser, "Electrode Models for electric current computed tomography," IEEE Transactions on Biomedical Engineering, vol. 36, no. 9, pp. 918-924, 1989. https://doi.org/10.1109/10.35300
  10. P. C. Hansen and D. P. O'Leary, "The use of the L-curve in the regularization of discrete ill-posed problems," SIAM J Sci Comput, vol. 14, pp.1487-1503, 1993. https://doi.org/10.1137/0914086
  11. M. H. Jeon and K. Y. Kim, "Application of Matrix Adaptive Regularization Method for Human Thorax Image Reconstruction," j.inst.Korean.electr.electron.eng, vol.19, no.1, pp.33-40, 2015.