Skip to main content
SpringerLink
Log in

Despeckling

  • Chapter
  • First Online:
Ultrasound and Carotid Bifurcation Atherosclerosis

Abstract

The wide use of ultrasound imaging equipment, including mobile and portable telemedicine ultrasound scanning instruments and computer-aided systems, necessitates the need for better image processing techniques, in order to offer a clearer image to the medical practitioner. This makes the use of efficient despeckle filtering an important task.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lamont D et al. Risk of cardiovascular disease measured by carotid intima-media thickness at age 49–51: life course study. BMJ. 2000;320:273–278.

    Article  PubMed  CAS  Google Scholar 

  2. Burckhardt CB. Speckle in ultrasound B-mode scans. IEEE Trans Sonics Ultrason. 1978;25(1):1–6.

    Article  Google Scholar 

  3. Wagner RF, Smith SW, Sandrik JM, Lopez H. Statistics of speckle in ultrasound B-scans. IEEE Trans Sonics Ultrason. 1983;30:156–163.

    Article  Google Scholar 

  4. Goodman JW. Some fundamental properties of speckle. J Opt Soc Am. 1976;66(11):1145–1149.

    Article  Google Scholar 

  5. Yongjian Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process. 2002;11(11):1260–1270.

    Article  Google Scholar 

  6. Prager RW, Gee AH, Treece GM, Berman L. Speckle Detection in Ultrasound Images Using First Order Statistics. Cambridge: University of Cambridge; 2002:1–17.

    Google Scholar 

  7. Loizou CP et al. Despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control. 2005;52(10):1653–1669.

    Article  PubMed  Google Scholar 

  8. Loizou CP, Pattichis CS, Pantziaris MS, Tyllis T, Nicolaides AN. Quantitative quality evaluation of ultrasound imaging in the carotid artery. Med Biol Eng Comput. 2006;44(5):414–426.

    Article  PubMed  CAS  Google Scholar 

  9. Loizou CP, Pattichis CS. Despeckle filtering algorithms and software for ultrasound imaging. San Rafael: Morgan & Claypool; 2008 (Spanias A, ed. Synthesis lectures on algorithms and software for engineering; #1).

    Google Scholar 

  10. Busse L, Crimmins TR, Fienup JR. A model based approach to improve the performance of the geometric filtering speckle reduction algorithm. IEEE Ultrason Symp. 1995;2:1353–1356.

    Google Scholar 

  11. Lee JS. Speckle analysis and smoothing of synthetic aperture radar images. Comput Graphics Image Process. 1981;17:24–32.

    Article  Google Scholar 

  12. Kuan DT, Sawchuk AA, Strand TC, Chavel P. Adaptive restoration of images with speckle. IEEE Trans Acoust Speech Signal Process. 1989;35(3):373–383.

    Article  Google Scholar 

  13. Insana M, Hall TJ, Gledndon GC, Posental SJ. Progress in quantitative ultrasonic imaging. SPIE Proceedings on Medical Imaging III: Image Formation 1989:1092–1099.

    Google Scholar 

  14. Frost VS, Stiles JA, Shanmungan KS, Holtzman JC. A model for radar images and its application for adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell. 1982;4(2):157–165.

    Article  PubMed  CAS  Google Scholar 

  15. Lee JS. Digital image enhancement and noise filtering by using local statistics. IEEE Trans Pattern Anal Mach Intell. 1980;2(2):165–168.

    Article  PubMed  CAS  Google Scholar 

  16. Lee JS. Refined filtering of image noise using local statistics. Comput Graphics Image Process. 1981;15:380–389.

    Article  Google Scholar 

  17. Kuan DT, Sawchuk AA, Strand TC, Pierre C. Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans Pattern Anal Mach Intell. 1985;7(2):165–177.

    Article  PubMed  CAS  Google Scholar 

  18. Christodoulou CI, et al. Despeckle filtering in ultrasound imaging of the carotid artery. In: Conference Proceedings of the 2nd Joint EMBS-BMES Conference; 2002; Houston; vol. 2:1027–1028.

    Google Scholar 

  19. Solbo S, Eltoft T. Homomorphic wavelet based-statistical despeckling of SAR images. IEEE Trans Geosci Remote Sensing. 2004;42(4):711–721.

    Article  Google Scholar 

  20. Saniie J, Wang T, Bilgutay N. Analysis of homomorphic processing for ultrasonic grain signal characterization. IEEE Trans Ultrason Ferroelectr Freq Control. 1989;3:365–375.

    Article  Google Scholar 

  21. Jin S, Wang Y, Hiller J. An adaptive non-linear diffusion algorithm for filtering medical images. IEEE Trans Inf Technol Biomed. 2000;4(4):298–305.

    Article  PubMed  CAS  Google Scholar 

  22. Weickert J, Romery B, Viergever M. Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process. 1998;7:398–410.

    Article  PubMed  CAS  Google Scholar 

  23. Rougon N, Preteux F. Controlled anisotropic diffusion. In: Conference on Nonlinear Image Processing VI, IS&T/SPIE Symposium on Electron Imaging, Science and Technology; San Jose; 1995:1–12.

    Google Scholar 

  24. Black M, Sapiro G, Marimont D, Heeger D. Robust anisotropic diffusion. IEEE Trans Image Process. 1998;7(3):421–432.

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  26. Abd-Elmoniem K, Youssef A-B, Kadah Y. Real-time speckle reduction and coherence Enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Trans Biomed Eng. 2002;49(9):997–1014.

    Article  PubMed  Google Scholar 

  27. Zhong S, Cherkassky V. Image denoising using wavelet thresholding and model selection. In: Proceedings of the IEEE International Conference on Image Processing; 2000; Vancouver:1–4.

    Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  29. Zong X, Laine A, Geiser E. Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Trans Med Imaging. 1998;17(4):532–540.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  31. Donoho DL. Denoising by soft thresholding. IEEE Trans Inf Theory. 1995;41:613–627.

    Article  Google Scholar 

  32. Wink AM, Roerdink JBTM. Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing. IEEE Trans Med Imaging. 2004;23(3):374–387.

    Article  PubMed  Google Scholar 

  33. Dutt V. Statistical analysis of ultrasound echo envelope [Ph.D. dissertation]. Rochester: Mayo Graduate School; 1995.

    Google Scholar 

  34. Nagao M, Matsuyama T. Edge preserving smoothing. Comput Graphics Image Process. 1979;9:394–407.

    Article  Google Scholar 

  35. Huang T, Yang G, Tang G. A fast two-dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process. 1979;27(1):13–18.

    Article  Google Scholar 

  36. Ali SM, Burge RE. New automatic techniques for smoothing and segmenting SAR images. Signal Process. 1988;14:335–346.

    Article  Google Scholar 

  37. Gupta S, Chauhan RC, Sexana SC. Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med Biol Eng Comput J. 2004;42:189–192.

    Article  CAS  Google Scholar 

  38. A Philips Medical System Company. Comparison of image clarity, SonoCT real-time compound imaging versus conventional 2D ultrasound imaging. ATL Ultrasound, Report; 2001.

    Google Scholar 

  39. Elatrozy T et al. The effect of B-mode ultrasonic image standardization of the echodensity of symptomatic and asymptomatic carotid bifurcation plaque. Int Angiol. 1998;17(3):179–186.

    PubMed  CAS  Google Scholar 

  40. Haralick RM, Shanmugam K, Dinstein I. Texture features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610–621.

    Article  Google Scholar 

  41. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides AN. Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging. 2003;22(7):902–912.

    Article  PubMed  CAS  Google Scholar 

  42. Winkler S. Vision Models and Quality Metrics for Image Processing Applications [Ph.D.]. Lausanne: University of Lausanne-Switzerland; 2000.

    Google Scholar 

  43. Krupinski E, Kundel H, Judy P, Nodine C. The medical image perception society, key issues for image perception research. Radiology. 1998;209:611–612.

    PubMed  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  45. Sakrison D. On the role of observer and a distortion measure in image transmission. IEEE Trans Commun. 1977;25: 1251–1267.

    Article  Google Scholar 

  46. Loizou CP, Pattichis CS, Pantziaris M, Nicolaides AN. An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Trans Inf Technol Biomed. 2007;11(5):661–667.

    Article  PubMed  Google Scholar 

  47. Loizou CP, Pattichis CS, Nicolaides AN, Pantziaris M. Manual and automated media and intima thickness measurements of the common carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control. 2009;56(5):983–994.

    Article  PubMed  Google Scholar 

  48. Chen TJ et al. A novel image quality index using Moran I statistics. Phys Med Biol. 2003;48:131–137.

    Article  Google Scholar 

  49. Gonzalez R, Woods R. Digital Image Processing. 2nd ed. Upper Saddle River: Prentice-Hall Inc.; 2002:419–420.

    Google Scholar 

  50. Wang Z, Bovik A. A universal quality index. IEEE Signal Process Lett. 2002;9(3):81–84.

    Article  CAS  Google Scholar 

  51. Sheikh HR, Bovik AC, de Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process. 2005;14(12):2117–2128.

    Article  PubMed  Google Scholar 

  52. Loizou CP, Pattichis CS, Pantziaris MS, Tyllis T, Nicolaides AN. Snakes based segmentation of the common carotid artery intima media. Med Biol Eng Comput. 2007;45:35–49.

    Article  PubMed  CAS  Google Scholar 

  53. Pattichis CS et al. Cardiovascular: ultrasound imaging in vascular cases. In: Akay M, ed. Wiley Encyclopaedia of Biomedical Engineering. Hoboken: Wiley; 2006:1–12.

    Google Scholar 

  54. Loizou CP, Pantziaris M, Pattichis MS, Kyriakou E, Pattichis CS. Ultrasound image texture analysis of the intima and media layers of the common carotid artery and its correlation with age and gender. Comput Med Imaging Graphics. 2009;33(4):317–324.

    Article  CAS  Google Scholar 

  55. Jung J-H, Hong K, Yang S. Noise reduction using variance characteristics in noisy image sequence. In: International Conference on Consumer Electronics; 2005; Las Vegas:213–214.

    Google Scholar 

  56. Bertalmio M, Caselles V, Pardo A. Movie Denoising by average of warped lines. IEEE Trans Image Process. 2007;16(9):233–247.

    Article  Google Scholar 

  57. Zlokoliza V. Advanced Nonlinear Methods for Video Denoising [Ph.D. dissertation]. Belgium: Ghent University; 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, School of Sciences, Intercollege, Limassol, Cyprus

    Christos P. Loizou

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    Constantinos S. Pattichis

Authors
  1. Christos P. Loizou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Constantinos S. Pattichis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Imperial College, London, W2 1PG, United Kingdom

    Andrew Nicolaides

  2. , Dept. Surgery, University of Washington, Seattle, 98195-6410, Washington, USA

    Kirk W. Beach

  3. Dept. Computer Science, University of Cyprus, Kallipoleos Street 75, Nicosia, 1678, Cyprus

    Efthivoulos Kyriacou

  4. Dept. Computer Science, University of Cyprus, Kallipoleos Street 75, Nicosia, 1678, Cyprus

    Constantinos S. Pattichis

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Loizou, C.P., Pattichis, C.S. (2011). Despeckling. In: Nicolaides, A., Beach, K., Kyriacou, E., Pattichis, C. (eds) Ultrasound and Carotid Bifurcation Atherosclerosis. Springer, London. https://doi.org/10.1007/978-1-84882-688-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-688-5_6

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-687-8

  • Online ISBN: 978-1-84882-688-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation