Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Comparison of Despeckle Filters for Breast Ultrasound Images

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. K. Abd-Elmoniem, A. Youssef, Y. Kadah, Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Trans. Biomed. Eng. 49, 997–1014 (2002)

    Article  Google Scholar 

  2. S. Aja-Fernández, C. Alberola-López, On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image process. 15, 2694–2701 (2006)

    Article  Google Scholar 

  3. G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, A.M.L. Lanzolla, Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Measurement 45, 1792–1800 (2012)

    Article  Google Scholar 

  4. S. Balocco, C. Gatta, O. Pujol, J. Mauri, P. Radeva, Srbf: speckle reducing bilateral Filtering. Ultrasound Med. Biol. 36, 1353–1363 (2010)

    Article  Google Scholar 

  5. J.C. Bamber, C. Daft, Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images. Ultrasonics 24, 41–44 (1986)

    Article  Google Scholar 

  6. C.B. Burckhardt, Speckle in ultrasound B-mode scans. IEEE Trans. Sonics Ultrason. 25, 1–6 (1978)

    Article  Google Scholar 

  7. J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. 8, 679–698 (1986)

    Article  Google Scholar 

  8. D. Carol, S. Rebecca, B. Priti, J. Ahmedin, Breast cancer statistics, 2011. Cancer J. Clin. 61, 408–418 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  12. R.G. Dantas, E.T. Costa, S. Leeman, Ultrasound speckle and equivalent scatterers. Ultrasonics 43, 405–420 (2005)

    Article  Google Scholar 

  13. L. De Marchi, N. Testoni, N. Speciale, Prostate tissue characterization via ultrasound speckle statistics, in IEEE Internation Symposium on Signal Processing and Information Technology, 2006, pp. 208–211

  14. C. Deledalle, L. Denis, F. Tupin, Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 18, 2661–2672 (2009)

    Article  MathSciNet  Google Scholar 

  15. V. Dutt, Statistical analysis of ultrasound echo envelope. Ph.D. dissertation, Mayo Graduate School, Rochester, MN, 1995

  16. M. Elad, On the origin of the bilateral filter and ways to improve It. IEEE Trans. Image Process. 11, 1141–1151 (2002)

    Article  MathSciNet  Google Scholar 

  17. D. Fernandes, M. Sekine, Suppression of Weibull radar clutter. IEICE Trans. Commun. E76–B, 1231–1235 (1993)

    Google Scholar 

  18. S. Finn, M. Glavin, E. Jones, Echocardiographic speckle reduction comparison. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58, 82–101 (2011)

    Article  Google Scholar 

  19. V. Frost, J. Stiles, K. Shanmugan, J. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 4, 157–166 (1982)

    Article  Google Scholar 

  20. J.W. Goodman, Some fundamental properties of speckle. J. Opt. Soc. Am. 66, 1145–1149 (1976)

    Article  Google Scholar 

  21. S. Gupta, R.C. Chauhan, S.C. Sexana, Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using speckle modelling based on Rayleigh distribution. IEEE Proc. Vision Image Signal Process. 152, 129–135 (2005)

    Article  Google Scholar 

  22. http://field-ii.dk/?examples/cyst_phantom/cyst_phantom.html, last updated: 19:45 on Mon, 30 Apr 2012

  23. M. Insana, T.J. Hall, G.G. Cox, J.S. Rosenthal, Progress in quantitative ultrasonic imaging, in Proceedings of the SPIE Medical Imaging III, Image Formation, 1989, pp. 2–9

  24. M.F. Insana, R.F. Wagner, B.S. Garra, D.G. Brown, T.H. Shawker, Analysis of ultrasound image texture via generalized Rician statistics. Opt. Eng. 25, 743–748 (1986)

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. D. Kaplan, Q. Ma, on the statistical characteristics of the log-compressed Rayleigh signals: theorical formulation and experimental results. J. Acoust. Soc. Am. 95, 1396–1400 (1994)

    Article  Google Scholar 

  27. C. Kervrann, J. Boulanger, P. Coupé, Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal, in Proceedings of the Conference on Scale-Space and Variable Method Ischia, Italy, 2007, pp. 520–532

  28. K. Krissian, C. Westin, R. Kikinis, K.G. Vosburgh, Oriented speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 16, 1412–1424 (2007)

    Article  MathSciNet  Google Scholar 

  29. D.T. Kuan, A.A. Sawchuk, Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans. Pattern Anal. Machine Intell. 7, 165–177 (1985)

    Article  Google Scholar 

  30. D.T. Kuan, A.A. Sawchuk, T.C. Strand, P. Chavel, Adaptive restoration of images with speckle. IEEE Trans. Acoust. Speech Signal Process. 35, 373–383 (1987)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. C. Loizou, C. Pattichis, C. Christodoulou, R. Istepanian, M. Pantziaris, A. Nicolaides, Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 52, 1653–1669 (2005)

    Article  Google Scholar 

  33. A. Mittal, A.K. Moorthy, A.C. Bovik, No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  34. A. Mittal, R. Soundararajan, A.C. Bovik, Making a ‘completely blind’ image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)

    Article  Google Scholar 

  35. S. Parrilli, M. Poderico, C.V. Angelino, L. Verdoliva, A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50, 606–616 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. A. Pizurica, W. Philips, I. Lemahieu, M. Acheroy, A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans. Med Imaging 22, 323–331 (2003)

    Article  Google Scholar 

  38. W.K. Pratt, Digital Image Processing (Wiley, New York, 1978)

    Google Scholar 

  39. B.I. Raju, M.A. Srinivasan, Statistics of envelope of high-frequency ultrasonic backscatter from human skin in vivo. IEEE Trans. Ultrason. Ferroelect. Freq. Control 49, 871–882 (2002)

    Article  Google Scholar 

  40. P.M. Shankar, A general statistical model for ultrasonic backscattering from tissues. IEEE Trans. Ultrason. Ferroelect. Freq. Control 47, 727–736 (2000)

    Article  Google Scholar 

  41. P.C. Tay, S.T. Acton, J.A. Hossack, Ultrasound despeckle using an adaptive window stochastic approach, in Proceedings of the IEEE International Conference on Image Processing, 2006, 2549–2552

  42. C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in Proceedings of the Sixth International Conference on Computer Vision, 1998, pp. 839–846

  43. R. Wagner, S. Smith, J. Sandrik, H. Lopez, Statistics of speckle in ultrasound B-scans. IEEE Trans. Sonics Ultrason. 3, 156–163 (1983)

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Y. Yu, S.T. Acton, Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11, 1260–1270 (2002)

    Article  MathSciNet  Google Scholar 

  46. X. Zong, A.F. Laine, E.A. Geiser, Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Trans. Med. Imaging 17, 532–540 (1998)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that help greatly to improve the manuscript. The authors would also like to thank the people who provide the MATLAB code or executable file, especially Karl Krissian for his OSRAD filter, Sara Parrilli for her SAR-BM3-D filter, and Anish Mittal for his NIQE evaluator. The work is partially supported by the National Natural Science Foundation of China (60974042).

Author information

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, No. 288, Liuhe Road, Xihu District, Hangzhou , 310032, People’s Republic of China

    Ju Zhang & Chen Wang

  2. Department of Ultrasound, Zhejiang Hospital, No. 12 Lingyin Road, Xihu District, Hangzhou , 310013, People’s Republic of China

    Yun Cheng

Authors
  1. Ju Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Cheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Wang, C. & Cheng, Y. Comparison of Despeckle Filters for Breast Ultrasound Images. Circuits Syst Signal Process 34, 185–208 (2015). https://doi.org/10.1007/s00034-014-9829-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-014-9829-y

Keywords

Search

Navigation