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Real time ultrasound image denoising

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Abstract

Image denoising is the process of removing the noise that perturbs image analysis methods. In some applications like segmentation or registration, denoising is intended to smooth homogeneous areas while preserving the contours. In many applications like video analysis, visual servoing or image-guided surgical interventions, real-time denoising is required. This paper presents a method for real-time denoising of ultrasound images: a modified version of the NL-means method is presented that incorporates an ultrasound dedicated noise model, as well as a GPU implementation of the algorithm. Results demonstrate that the proposed method is very efficient in terms of denoising quality and is real-time.

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References

  1. Alvarez, L., Lions, P.L., Morel, J.M.: Image selective smoothing and edge detection by nonlinear diffusion, II. SIAM J. Numer. Anal. 29, 845–866 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  2. Argenti, F., Torricelli, G.: Speckle suppression in ultrasonic images based on undecimated wavelets. EURASIP J. Adv. Signal Process. 2003(5), 470–478 (2003)

    Article  MATH  Google Scholar 

  3. Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  4. Cates, J.E., Lefohn, A.E., Whitaker, R.T.: Gist: an interactive, gpu-based level set segmentation tool for 3D medical images. Med. Image Anal. 8(3), 217–231 (2004)

    Article  Google Scholar 

  5. Chan, T.F., Osher, S., Shen, J.: The digital TV filter and nonlinear denoising. IEEE Trans. Image Process. 10(2), 231–241 (2001)

    Article  MATH  Google Scholar 

  6. Chen, Y., Vemuri, B.C., Wang, L.: Image denoising and segmentation via nonlinear diffusion. Comput. Math. Appl. 39(5–6), 131–150 (2000)

    Article  MathSciNet  Google Scholar 

  7. Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. Image Process. 18(10), 2221–2229 (2009)

    Article  Google Scholar 

  8. Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise non local means denoising filter for 3D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)

    Article  Google Scholar 

  9. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  10. Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 2, 157–165 (1982)

    Article  Google Scholar 

  11. Hao, X., Gao, S., Gao, X.: A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Trans. Med. Imaging 18(9), 787–794 (1999)

    Article  Google Scholar 

  12. Huhle, B., Schairer, T., Jenke, P., Straßer, W.: Robust non-local denoising of colored depth data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008, pp. 1–7 (2008)

  13. Jensen, J.A.: Field: A program for simulating ultrasound systems. Med. Biol. Eng. Comput. 34, 351–353 (1996)

    Article  Google Scholar 

  14. Kervrann, C., Boulanger, J., Coupé, P.: Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. In: Proceedings of Conference on Scale-Space and Variational Methods (SSVM’ 07), pp. 520–532, Ischia, Italy, June 2007

  15. Kharlamov, A., Podlozhnyuk, V.: Image Denoising. http://developer.download.nvidia.com/compute/cuda/1_1/Website/projects/imageDenoising/doc/imageDenoising.pdf (2007)

  16. Krissian, K., Vosburgh, K., Kikinis, R., Westin, C.-F.: Speckle-constrained anisotropic diffusion for ultrasound images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

  17. Kuan, D.T., Sawchuck, A.A., Strand, T.C., Chavel, P.: Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. 7(2), 165–177 (1985)

    Article  Google Scholar 

  18. Kubias, A., Deinzer, F., Feldmann, T., Paulus, D., Schreiber, B., Brunner, T.: 2D/3D image registration on the gpu. Pattern Recognit. Image Anal. 18(3), 381–389 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Loupas, T., McDicken, W.N., Allan, P.L.: An adaptive weighted median filter for speckle suppression in medical ultrasound image. IEEE T. Circ. Syst. 36, 129–135 (1989)

    Article  Google Scholar 

  21. Motwani, M.C., Gadiya, M.C., Motwani, R.C., Harris, F.C. Jr.: Survey of image denoising techniques. In: Proceedings of GSPx, pp. 27–30. Citeseer (2004)

  22. Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  23. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  24. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  25. Tadmor, E., Nezzar, S., Vese, L.: A multiscale image representation using hierarchical (BV, L 2) decompositions. Multiscale Model. Simul. 2(4), 554–579 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  26. Tao, Z., Tagare, H.D., Beaty, J.D.: Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Trans. Med. Imaging 25(11), 1483–1491 (2006)

    Article  Google Scholar 

  27. Tay, P.C., Acton, S.T., Hossack, J.A.: A stochastic approach to ultrasound despeckling. In: Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium, pp. 221–224 (2006)

  28. Tay, P.C., Acton, S.T., Hossack, J.A.: Ultrasound despeckling using an adaptive window stochastic approach. In: IEEE International Conference on Image Processing, pp. 2549–2552 (2006)

  29. Wachowiak, M.P., Elmaghraby, A.S., Smolíkova, R., Zurada, J.M.: Classification and estimation of ultrasound speckle noise with neural networks. In: IEEE International Symposium on Bio-Informatics and Biomedical Engineering (BIBE’00), pp. 245–252 (2000)

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Authors and Affiliations

  1. INRIA Centre de Recherche Rennes Bretagne Atlantique, 35042, Rennes Cedex, France

    Fernanda Palhano Xavier de Fontes, Guillermo Andrade Barroso, Pierrick Coupé & Pierre Hellier

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  1. Fernanda Palhano Xavier de Fontes
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  2. Guillermo Andrade Barroso
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  3. Pierrick Coupé
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  4. Pierre Hellier
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Correspondence to Pierre Hellier.

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Palhano Xavier de Fontes, F., Andrade Barroso, G., Coupé, P. et al. Real time ultrasound image denoising. J Real-Time Image Proc 6, 15–22 (2011). https://doi.org/10.1007/s11554-010-0158-5

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  • DOI: https://doi.org/10.1007/s11554-010-0158-5

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