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Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding

Abstract

We propose a novel framework for despeckling ultrasound image sequences while respecting the structural details. More precisely, we use thresholding in an adapted wavelet domain that jointly takes into account for the non-Gaussian statistics of the noise and the differences in spatial and temporal regularities. The spatiotemporal wavelet is obtained via the Kronecker product of two sparsifying wavelet bases acting, respectively, on the spatial and temporal domains. Besides enabling a structured sparse representation of the time–space plan, it also makes it possible to perform a variance stabilization routine on the spatial domain through a Fisz transformation. The proposed method enjoys adaptability, easy tuning and theoretical guaranties. We propose the corresponding algorithm together with results that demonstrate the benefits of the proposed spatiotemporal approach over the successive spatial treatment. Finally, we describe a data-driven extension of the proposed method that is based on temporal pre-filtering.

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Notes

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    Available on https://sites.google.com/site/pierrickcoupe/softwares/denoising-for-medical-imaging/speckle-reduction/obnlm-package.

References

  1. 1.

    Abbott, J.G., Thurstone, F.: Acoustic speckle: theory and experimental analysis. Ultrason. Imaging 1(4), 303–324 (1979)

  2. 2.

    Achim, A., Bezerianos, A., Tsakalides, P.: Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20(8), 772–783 (2001)

  3. 3.

    Amiot, C., Girard, C., Chanussot, J., Pescatore, J., Desvignes, M.: Spatio-temporal multiscale denoising of fluoroscopic sequence. IEEE Trans. Med. Imaging 35(6), 1565–1574 (2016)

  4. 4.

    Clarysse, P., Tafazzoli, J., Delachartre, P., Croisille, P.: Simulation based evaluation of cardiac motion estimation methods in tagged-MR image sequences. J. Cardiovasc. Magn. Reson. 13(Suppl 1), P360 (2011)

  5. 5.

    Coifman, R.R., Donoho, D.L.: Translation-invariant denoising. In: Antoniadis and Oppenheim, Wavelets and Statistics. Lecture Notes in Statistics, pp. 125–150 (1995)

  6. 6.

    Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. Image Process. 18(7), 2221–2229 (2009)

  7. 7.

    Duarte, M.F., Baraniuk, R.G.: Kronecker compressive sensing. IEEE Trans. Image Process. 21(2), 494–504 (2012)

  8. 8.

    Farouj, Y., Freyermuth, J.M., Navarro, L., Clausel, M., Delachartre, P.: Hyperbolic wavelet-Fisz denoising for a model arising in ultrasound imaging. IEEE Trans. Comput. Imaging 3(1), 1–10 (2017)

  9. 9.

    Forouzanfar, M., Moghaddam, H.A., Gity, M.: A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images. SIViP 4(3), 359–375 (2010)

  10. 10.

    Fryzlewicz, P.: Data-driven wavelet-Fisz methodology for nonparametric function estimation. Electron. J. Stat. 2, 863–896 (2008)

  11. 11.

    Gifani, P., Behnam, H., Sani, Z.A.: Noise reduction of echocardiographic images based on temporal information. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61(4), 620–630 (2014)

  12. 12.

    Gupta, D., Anand, R.S., Tyagi, B.: Despeckling of ultrasound medical images using ripplet domain nonlinear filtering. SIViP 9(5), 1093–1111 (2015)

  13. 13.

    Jidesh, P., Bini, A.A.: Image despeckling and deblurring via regularized complex diffusion. SIViP 11(6), 977–984 (2017)

  14. 14.

    Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2(2), 165–168 (1980)

  15. 15.

    Loupas, T., McDicken, W., Allan, P.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Circuits Syst. 36, 129–135 (1989)

  16. 16.

    Motakis, E., Nason, G.P., Fryzlewicz, P., Rutter, G.: Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach. Bioinformatics 22(20), 2547–2553 (2006)

  17. 17.

    Nadaraya, E.A.: On estimating regression. Theory Probab. Appl. 9(1), 141–142 (1964)

  18. 18.

    Nason, G.: Wavelet Methods in Statistics with R. Springer Science & Business Media, Berlin (2010)

  19. 19.

    Tenbrinck, D., Schmid, S., Jiang, X., Schäfers, K.P., Stypmann, J.: Histogram-based optical flow for motion estimation in ultrasound imaging. J. Math. Imaging Vis. 47(1–2), 138–150 (2013)

  20. 20.

    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

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Acknowledgements

This work was supported by “Région Rhône-Alpes” under the ARC 6. L. Navarro’s research was supported by the (ANR) under reference ANR-15-CE19-0002 (LBSMI). P. Delachartre was within the framework of the Labex CELYA (ANR-10-LABX-0060) and Labex PRIMES (ANR-11-LABX-0063) of the Université de Lyon.

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Correspondence to Younes Farouj.

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Farouj, Y., Navarro, L., Freyermuth, J. et al. Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding. SIViP 12, 1125–1132 (2018) doi:10.1007/s11760-018-1260-6

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Keywords

  • Dynamic ultrasound imaging
  • Despeckling
  • Fisz transformation
  • Variance stabilization
  • Structured sparsity