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“Bayesian 2D Deconvolution: A Model for Diffuse Ultrasound Scattering”

Authors: Oddvar Husby, Torgrim Lie, Thomas Langø, Jørn Hokland and Håvard Rue,
Affiliation: SINTEF and NTNU
Reference: 2001, Vol 22, No 4, pp. 227-242.

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Keywords: Medical ultrasound, restoration, Markov random fields, diffuse scattering, Markov chain Monte Carlo

Abstract: Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we have developed a new statistical model for diffuse scattering in 2D ultrasound radio-frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio-frequency ultrasound images are presented and compared with results produced by Wiener filtering.

PDF PDF (2391 Kb)        DOI: 10.4173/mic.2001.4.3





References:
[1] BESAG, J. (1974). Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society, Series B, vol. 36, pp. 192-236, with discussion.
[2] BESAG, J. GREEN, E (1993). Spatial statistics and Bayesian computation, Journal of the Rojal Statistical Society, Series B. vol. 16, pp. 395-407.
[3] BESAG, J., GREEN, P., HIGDON, D. MENGERSEN, K. (1995). Bayesian computation and stochastic systems, with discussion. Statistical Science, vol. 10, no. 1, pp. 3-66 doi:10.1214/ss/1177010123
[4] BESAG, J. KOOPERBERG, C. (1995). On conditional and intrinsic autoregressions, Biometrika, vol. 82, no. 4, pp. 733-746, December.
[5] BROOKS, S.P. ROBERTS, G.O. (1998). Assessing convergence of Markov chain Monte Carlo algorithms, Statistics and Computing, vol. 8, no. 4, pp. 319-335 doi:10.1023/A:1008820505350
[6] CHARBONNIER, P., BLANC-FERAUD, L., AUBERT, G. BARLAUD, M. (1997). Deterministic edge-preserving regularization in computed imaging, IEEE Transaction on Image Processing, vol. 6, no. 2, pp. 298-311, February doi:10.1109/83.551699
[7] COHEN, E S., GEORGIOU, G. HALPERN, E.J. (1997). WOLD decomposition of the backscatter echo in ultrasound images of soft tissue organs, IEEE Trans on Ultrason. Ferroelec. Frec. Contr.
[8] GEORGIOU, G. COHEN, F.S. (1998). Statistical characterization of diffuse scattering in ultrasound images, IEEE Trans. on Ultrason. Frec. Contr., vol. 45, no. 3, pp. 54-64.
[9] GEMAN, S. GEMAN, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 6, pp. 721-741 doi:10.1109/TPAMI.1984.4767596
[10] GEMAN, S. MCCLURE, D.E. (1987). Statistical methods for tomographic image reconstruction, In Proc. 46th: Sess. Int. Stat. Inst. Bulletin ISI, vol. 52.
[11] GEMAN, D. REYNOLDS, G. (1992). Constrained restoration and the recovery of discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 3, pp. 367-383, 3.
[12] GEMAN, D., REYNOLDS, G. YANG, C. (1993). Stochastic algorithms for restricted image spaces and experiments in deblurring, In Markov Random Fields: Theory and Application, Chellappa, R. and Jain, A., Eds. Academic Press, New York.
[13] GILKS, W.R., RICHARDSON, S. SPIEGELHALTER, D.J. (1996). Markov Chain Monte Carlo in Practice, Chapman and Hall.
[14] GOODMAN, J.W. (1975). Statistical properties of laser speckle patterns, In Laser Speckle and Related Phenomena, J.C. Dainty, Ed. Springer Verlag, Berlin doi:10.1007/BFb0111436
[15] GREEN, P.J. (1995). Reversible jump MCMC computation and Bayesian model determination, Biometrika, vol. 82, no. 4, pp. 711-732 doi:10.1093/biomet/82.4.711
[16] HASTINGS W.K. (1970). Monte Carlo simulation methods using Markov chains and their applications, Biometrika, vol. 57, pp. 97-109 doi:10.1093/biomet/57.1.97
[17] HIGDON, D.M. (1998). Auxiliary variable methods for Markov chain Monte Carlo with applications, Journal of the American Statistical Association, vol. 93, no. 442, pp. 585-595. June doi:10.2307/2670110
[18] HOKLAND, J.H. KELLY, P. A. (1996). Markov models of specular and diffuse scattering in restoration of medical ultrasound images, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 43, no. 4, pp. 660-669, July doi:10.1109/58.503728
[19] HURN, M. JENNISON, C. (1996). An extension of Geman and Reynolds´ approach to constrained restoration and the recovery of discontinuities, IEEE Transactions. on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 657-662, June doi:10.1109/34.506418
[20] IRACA, D., LANDINI, L. VERRAZZANI, L. (1989). Power spectrum equalization for ultrasonic image restoration, IEEE Trans. on Ultrason. Ferroelec. Frec. Contr, vol. 36, no. 2, pp. 216-222, March doi:10.1109/58.19154
[21] JAIN, A.K. (1989). Fundamentals of Digital Image Processing, Prentice-Hall.
[22] JENSEN, J.A. LEEMAN, S. (1994). Nonparametric estimation of ultrasound pulses, IEEE Transactions on Biomedical Engineering, vol. 41, no. 10, pp. 929-936 doi:10.1109/10.324524
[23] LANGØ, T, LIE, T., HUSBY, O. HOKLAND, J. (2001). Bayesian 2D deconvolution: Effect of using spatially invariant ultrasound point spread functions, IEEE Trans Ultrason Ferroelect Freq Control, vol. 48, no. 1, pp. 131-141 doi:10.1109/58.895920
[24] MARDIA, K.V., KENT, J.T. BIBBY, J.M. (1979). Multivariate Analysis, Academic Press, 1979.
[25] MENGERSEN, K.L., ROBERT, C.P. GUIHENNEUC-JOUYAUX, C. (1998). MCMC convergence diagnosics: a ´review´, Tech. Rep., CREST, INSEE, Paris, May.
[26] ØDEGÅRD, L.A. (1995). Phase aberration correction in medical ultrasound imaging, Ph.D. thesis, The Norwegian Institute of Technology.
[27] ROBERT, C.P. (1995). Convergence control methods for MCMC algorithms, Statistical Science, vol. 10, no. 3, pp. 231-253 doi:10.1214/ss/1177009937
[28] ROBERTS, G.O., GELMAN, A. GILKS, W.R. (1997). Weak convergence and optimal scaling of random walk metropolis algorithms, Ann. Appl Prob., vol. 7, pp. 110-120 doi:10.1214/aoap/1034625254
[29] TAXT, T. (1995). Restoration of medical ultrasound images using 2-dimensional homomorphic deconvolution, IEEE Trans. on Ultrason. Free Contr., vol. 42, no. 4, pp. 543-554, June.
[30] TAXT, T. FROLOVA, G. (1999). Noise robust one-dimensional blind deconvolution of medical ultrasound images, IEEE Trans. on Ultrason. Ferroelec. Free. Contr., vol. 46, no. 2, pp. 291-299, March doi:10.1109/58.753017
[31] WAGNER, R.F., INSANA, M.F. BROWN, D.G. (1987). Statistical properties of radio-frequency and envelope-detected signals with application to medical ultrasound, J. Opt. Soc. Am, A, vol. 4, no. 5, pp. 910-922.
[32] WINKLER, G. (1995). Image Analysis, Random Fields and Dynamic Monte Carlo Methods, Springer, Berlin.
[33] ZONG, X., LAINE, A. F. GEISER, E.A. (1998). Speckle reduction and contrast enhencement of echocardiograms via multiscale nonlinear processing, IEEE Transaction on Medical Imaging, vol. 17, no. 4, pp. 532-540, August doi:10.1109/42.730398


BibTeX:
@article{MIC-2001-4-3,
  title={{Bayesian 2D Deconvolution: A Model for Diffuse Ultrasound Scattering}},
  author={Husby, Oddvar and Lie, Torgrim and Langø, Thomas and Hokland, Jørn and Rue, Håvard},
  journal={Modeling, Identification and Control},
  volume={22},
  number={4},
  pages={227--242},
  year={2001},
  doi={10.4173/mic.2001.4.3},
  publisher={Norwegian Society of Automatic Control}
};

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