Despeckling of Ultrasound Images of Bone Fracture Using RADWT Based Non-Linear Filtering

  • Deep GuptaEmail author
  • Radhey Shyam Anand
  • Barjeev Tyagi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


Despeckling in ultrasound medical images is of great interest. Due to presence of speckle, experts may not be able to extract correct and useful information from the images. This chapter presents a method for despeckling based on a new rational-dilation wavelet transform (RADWT) and non-linear bilateral filter (BLF). The RADWT, a new family of the discrete wavelet transform for which frequency resolution can be varied, provides effective representation of the noisy coefficients. Bilateral filter and thresholding scheme are applied to the noisy RADWT coefficients to improve the denoising efficiency and preserve the edge features effectively. The proposed method also helps to improve the visual quality of bone fracture ultrasound images. The performance of the proposed method is evaluated on the different ultrasound images of bone fracture and results show significant improvement not only in the speckle reduction but also in the edge preservation performance.


Bilateral filter (BLF) Bone fracture Rational-dilation wavelet transform (RADWT) Speckle Thresholding Ultrasound 


  1. 1.
    Dhawan AP (2003) Medical image analysis. Wiley Inc., New YorkGoogle Scholar
  2. 2.
    Lazović D, Wegner U, Peters G, Gossé F (1996) Ultrasound for diagnosis of apophyseal injuries. Knee Surg Sports Traumatol Arthrosc 3:234–237CrossRefGoogle Scholar
  3. 3.
    Hübner U, Schlicht W, Outzen S, Barthel M, Halsband H (2000) Ultrasound in the diagnosis of fractures in children. J Bone Joint Surg Br 82(8):1170–1173CrossRefGoogle Scholar
  4. 4.
    Rathfelder FJ, Paar O (1995) Possibilities for using sonography as a diagnostic procedure in fractures during the growth period. Der Unfallchirurg 98(12):645–649Google Scholar
  5. 5.
    Heiner JD, Proffitt AM, McArthur TJ (2011) The ability of emergency nurses to detect simulated long bone fractures with portable ultrasound. Int Emerg Nurs 19(3):120–124CrossRefGoogle Scholar
  6. 6.
    Heiner JD, McArthur TJ (2009) A simulation model for the ultrasound diagnosis of long-bone fractures. Simul Healthc 4(4):228–231CrossRefGoogle Scholar
  7. 7.
    Marshburn TH, Legome E, Sargsyan A, Li SMJ, Noble VA, Dulchavsky AS, Sims C, Robinson D (2004) Goal-directed ultrasound in the detection of long-bone fractures. J Trauma Acute Care Surg 57(2):329–332CrossRefGoogle Scholar
  8. 8.
    Elamvazuthi I, Zain MLBM, Begam KM (2013) Despeckling of ultrasound images of bone fracture using multiple filtering algorithms. Math Comput Model 57(1–2):152–168CrossRefGoogle Scholar
  9. 9.
    Bitschnau R, Gehmacher O, Kopf A, Scheier M, Mathis G (1996) Ultrasonography in the diagnosis of rib and sternal fracture. Eur J Ultrasound 3(2):197–297CrossRefGoogle Scholar
  10. 10.
    Griffith JF, Rainer TH, Ching AS, Law KL, Cocks RA, Metreweli C (1999) Sonography compared with radiography in revealing acute rib fracture. Am J Roentgenol 173(6):1603–1609CrossRefGoogle Scholar
  11. 11.
    Hurley ME, Keye GD, Hamilton S (2004) Is ultrasound really helpful in the detection of rib fractures? Injury 35(6):562–566CrossRefGoogle Scholar
  12. 12.
    Mittal D, Kumar V, Saxena SC, Khandelwal N, Karla N (2010) Enhancement of the ultrasound images by modified anisotropic diffusion method. Biol Eng Comput 48(12):1281–1291CrossRefGoogle Scholar
  13. 13.
    Pratt WK (2006) Digital image processing. Wiley, New YorkGoogle Scholar
  14. 14.
    Loupas T (1989) An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans Circuits Syst 36(1):129–135CrossRefGoogle Scholar
  15. 15.
    Gonzalez RC, Woods RE (2001) Digital image processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  16. 16.
    Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  17. 17.
    Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans Pattern Anal Mach Intell 7(2):165–177CrossRefGoogle Scholar
  18. 18.
    Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2(2):165–168CrossRefGoogle Scholar
  19. 19.
    Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11(11):1260–1270MathSciNetCrossRefGoogle Scholar
  20. 20.
    Liu X, Liu J, Xu X, Chun L, Deng Y (2011) A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images. BMC Genomics 12:1–10CrossRefGoogle Scholar
  21. 21.
    Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90:1200–1224MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Donoho DL (1995) De-noising by soft thresholding. IEEE Trans Inf Theory 41(3):613–627MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Gupta S, Chauhan RC, Saxena SC (2004) Wavelet based statistical approach for speckle reduction in medical ultrasound images. Med Biol Eng Comput 42:189–192CrossRefGoogle Scholar
  25. 25.
    Foder IK, Kamath C, Kamath R (2001) Denoising through wavelet shrinkage: an empirical study. J Electron Imag 12:151–160CrossRefGoogle Scholar
  26. 26.
    Achim A, Bezerianos A, Tsakalides P (2001) Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans Med Imag 20(8):772–783CrossRefGoogle Scholar
  27. 27.
    Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Michailovich OV, Tannenbaum A (2006) Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 53(1):64–78CrossRefGoogle Scholar
  29. 29.
    Bhutada GG, Anand RS, Saxena SC (2010) Fast adaptive learning algorithm for sub-band adaptive thresholding function in image denoising. Int J Comput Intell Stud 1(3):227–241CrossRefGoogle Scholar
  30. 30.
    Abrahim BA, Kadah Y (2011) Speckle noise reduction method combining total variation and wavelet shrinkage for clinical ultrasound imaging. In: Proceedings of 1st middle east conference biomedical engineering (MECBME), pp 80–83Google Scholar
  31. 31.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of sixth international conference computer vision, pp 839–846Google Scholar
  32. 32.
    Vanithamani R, Umamaheswari G (2011) Wavelet based despeckling of medical ultrasound images with bilateral filter. In: Proceedings of IEEE region 10 Conference TENCON, pp 389–393Google Scholar
  33. 33.
    Anand CS, Sahambi JS (2010) Wavelet domain non-linear filtering for MRI denoising. Magn Reson Imaging 28(6):842–861CrossRefGoogle Scholar
  34. 34.
    Bayram I, Selesnick IW (2009) Frequency-domain design of overcomplete rational-dilation wavelet transforms. IEEE Trans Signal Process 57(8):2957–2972MathSciNetCrossRefGoogle Scholar
  35. 35.
    Gupta D, Anand RS, Tyagi B (2012) Enhancement of medical ultrasound images using non-linear filtering based on rational-dilation wavelet transform, lecture notes in engineering and computer science. In: Proceedings of the world congress on engineering and computer science 2012, WCECS 2012, San Francisco, USA, 24–26 Oct 2012, pp 615–620Google Scholar
  36. 36.
    Zhang M, Gunturk BK (2008) Multiresolution bilateral filtering for image denoising. IEEE Trans Image Process 17(12):2324–2332MathSciNetCrossRefGoogle Scholar
  37. 37.
    Tang J, Guo S, Sun Q, Deng Y, Zhou D (2010) Speckle reducing bilateral filter for cattle follicle segmentation. BMC Genomics 11(2):1–9Google Scholar
  38. 38.
    Denweng Z, Wengang C (2008) Image denoising with an optimal threshold and neighbouring window. Pattern Recognit Lett 29:1694–1697CrossRefGoogle Scholar
  39. 39.
    Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84CrossRefGoogle Scholar
  40. 40.
    Thakur A, Anand RS (2005) Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction. Digit Signal Process 15:455–465CrossRefGoogle Scholar
  41. 41.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Deep Gupta
    • 1
    Email author
  • Radhey Shyam Anand
    • 1
  • Barjeev Tyagi
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of Technology RoorkeeRoorkeIndia

Personalised recommendations