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http://dx.doi.org/10.7471/ikeee.2016.20.3.226

Performance Comparison of Regularization Methods in Electrical Resistance Tomography  

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)
Publication Information
Journal of IKEEE / v.20, no.3, 2016 , pp. 226-234 More about this Journal
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.
Keywords
electrical resistance tomography; ill-posedness of inverse problem; regularization; l2-norm; l1-norm; total variation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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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.   DOI
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.   DOI
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.   DOI
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.