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Wavelet Based Local Coherent Tomography with an Application in Terahertz Imaging

  • Xiao-Xia Yin
  • Brian W. -H. Ng
  • Bradley Ferguson
  • Derek Abbott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Terahertz Computed Tomography (THz-CT) is a form of optical coherent tomography, which offers a promising approach for achieving non-invasive inspection of solid materials, with potentially numerous applications in industrial manufacturing and biomedical engineering. With traditional CT techniques such as X-ray tomography, full exposure data are needed to produce cross sectional images, even if the region of interest is small. For time-domain terahertz measurements, the requirement for full exposure data is impractical due to the slow measurement process. In this paper, we apply a wavelet-based algorithm to locally reconstruct THz-CT images with a significant reduction in the required measurements. The algorithm recovers an approximation of the region of interest from terahertz measurements within its vicinity, and thus improves the feasibility of using terahertz imaging to detect defects in solid materials and diagnose disease states for clinical practice.

Keywords

Projection Angle Terahertz Pulse Ramp Filter Local Reconstruction Optical Coherent Tomography 
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.

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References

  1. 1.
    Delaney, A.H., Bresler, Y.: Multiresolution tomographic reconstruction using wavelets. IEEE Transactions on Image Processing 4(6), 799–813 (1995)CrossRefGoogle Scholar
  2. 2.
    Ferguson, B., Wang, S., Gray, D., Abbott, D., Zhang, X.C.: Identification of biological tissue using chirped probe THz imaging. Microelectronics Journal 33(12), 1043–1051 (2002)CrossRefGoogle Scholar
  3. 3.
    Ferguson, B., Wang, S., Gray, D., Abbott, D., Zhang, X.C.: T-ray computed tomography. Optics Letters 27(15), 1312–1314 (2002)CrossRefGoogle Scholar
  4. 4.
    Galväo, R.K.H., Hadjiloucas, S., Bowen, J.W., Coelho, C.: Optimal discrimination and classification of thz spectra in the wavelet domain. Opt. Express 11, 1462–1473 (2003)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Inc., New Jersey (2002)Google Scholar
  6. 6.
    Hanson, K.M., Wecksung, G.W.: Bayesian estimation of 3-D objects from few radiographs. Journal of Optical Society of America 73(11), 1501–1509 (1983)CrossRefGoogle Scholar
  7. 7.
    Mittleman, D., Neelamani, R., Baraniuk, R.G., Rudd, J.V., Koch, M.: Recent advances in terahertz imaging. Appl. Phys. B. 68, 1085–1094 (1999)CrossRefGoogle Scholar
  8. 8.
    Rashid-Farrokhi, F., Liu, K.J.R., Berenstein, C.A., Walnut, D.: Wavelet-based multiresolution local tomography. IEEE Transactions on Image Processing 6(10), 1412–1430 (1997)CrossRefGoogle Scholar
  9. 9.
    Siegel, H.P.: Terahertz technology in biology and medicine. IEEE transactions on microwave theory and techniques 52(10), 2438–2447 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiao-Xia Yin
    • 1
  • Brian W. -H. Ng
    • 1
  • Bradley Ferguson
    • 2
  • Derek Abbott
    • 1
  1. 1.Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, The University of Adelaide, SA 5005Australia
  2. 2.Tenix Defence Systems Pty Ltd, Technology Park, Mawson Lakes, SA 5095 

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