Advertisement

Wavelet and Total Variation Based Method Using Adaptive Regularization for Speckle Noise Reduction in Ultrasound Images

  • Nishtha Rawat
  • Manminder Singh
  • Birmohan SinghEmail author
Article
  • 28 Downloads

Abstract

Ultrasound (US) images are useful in medical diagnosis. US is preferred over other medical diagnosis technique because it is non-invasive in nature and has low cost. The presence of speckle noise in US images degrades its usefulness. A method that reduces the speckle noise in US images can help in correct diagnosis. This method also should preserve the important structural information in US images while removing the speckle noise. In this paper, a method for removing speckle noise using a combination of wavelet, total variation (TV) and morphological operations has been proposed. The proposed method achieves denoising by combining the advantages of the wavelet, TV and morphological operations along with the utilization of adaptive regularization parameter which controls the amount of smoothing during denoising. The work in this paper has the capability of reducing speckle noise while preserving the structural information in the denoised image. The proposed method demonstrates strong denoising for synthetic and real ultrasound images, which is also supported by the results of various quantitative measures and visual inspection.

Keywords

Speckle noise Ultrasound images Wavelet transforms Total variation Morphological operations 

Abbreviation

US

Ultrasound

TV

Total variation

SRAD

Speckle reducing anisotropic diffusion

PM

Perona Malik

FOSRAD

Faster oriented speckle reducing anisotropic diffusion

PSNR

Peak signal to noise ratio

MSE

Mean squared error

RMSE

Root mean squared error

UQI

Universal Quality Index

SNR

Signal to noise ratio

MAE

Mean absolute error

FSIM

Feature Similarity Index Metric

SSI

Speckle Suppression Index

MPSSI

Mean Preservation Speckle Suppression Index

SMPI

Speckle Suppression and Mean Preservation Index

NK

Normalized correlation

AD

Average difference

NAE

Normalized absolute error

SSIM

Structural Similarity Index Metric

FOPDE

Fourth order partial differential equations

MTV

Modified total variation

SB

Split Bregman

Notes

References

  1. 1.
    Gupta, D., Anand, R. S., & Tyagi, B. (2014). Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images. Biomedical Signal Processing and Control, 10(1), 79–91.Google Scholar
  2. 2.
    Hiremath, P. S., Akkasaligar, P. T., & Badiger, S. (2013). Speckle noise reduction in medical ultrasound images. In Advancements and breakthroughs in ultrasound imaging (pp. 201–241). Intech Publications.Google Scholar
  3. 3.
    Ragesh, N. K., Anil, A. R., & Rajesh, R. (2011). Digital image denoising in medical ultrasound images: A survey. In International conference on artificial intelligence and machine learning, (vol. 12, pp. 63–73).Google Scholar
  4. 4.
    Achim, A., Bezerianos, A., & Tsakalides, P. (2001). Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging, 20(8), 772–783.Google Scholar
  5. 5.
    Gupta, S., Chauhan, R. C., & Sexana, S. C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Medical & Biological Engineering & Computing, 42(2), 189–192.Google Scholar
  6. 6.
    Chen, Y., & Raheja, A. (2006). Wavelet lifting for speckle noise reduction in ultrasound images. Engineering in Medicine and Biology Society, 3, 3129–3132.Google Scholar
  7. 7.
    Sudha, S., Suresh, G. R., & Sukanesh, R. (2009). Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. International Journal of Computer Theory and Engineering, 1(1), 7–12.Google Scholar
  8. 8.
    Mateo, J. L., & Fernández-Caballero, A. (2009). Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Systems with Applications, 36(4), 7786–7797.Google Scholar
  9. 9.
    Sarode, M., & Deshmukh, P. (2011). Reduction of speckle noise and image enhancement of images using filtering technique. International Journal of Advancements in Technology, 2(1), 30–38.Google Scholar
  10. 10.
    Ruikar, S. D., & Doye, D. D. (2011). Wavelet based image denoising technique. International Journal of Advanced Computer Science and Applications, 2(3), 49–53.Google Scholar
  11. 11.
    Andria, G., Attivissimo, F., Cavone, G., Giaquinto, N., & Lanzolla, A. M. L. (2012). Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Measurement: Journal of the International Measurement Confederation, 45(7), 1792–1800.Google Scholar
  12. 12.
    Joel, T., & Sivakumar, R. (2013). Despeckling of ultrasound medical images: A survey. Journal of Image and Graphics, 1(3), 161–165.Google Scholar
  13. 13.
    Yadav, A. K., Roy, R., Kumar, A. P., Kumar, C. S., & Dhakad, S. K. (2015). De-noising of ultrasound image using discrete wavelet transform by symlet wavelet and filters. In International conference on advances in computing, communications and informatics, Kochi (pp. 1204–1208).Google Scholar
  14. 14.
    Zhang, J., Lin, G., Wu, L., & Cheng, Y. (2016). Speckle filtering of medical ultrasonic images using wavelet and guided filter. Ultrasonics, 65, 177–193.Google Scholar
  15. 15.
    Gai, S., Zhang, B., Yang, C., & Yu, L. (2018). Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution. Digital Signal Processing: A Review Journal, 72, 192–207.Google Scholar
  16. 16.
    Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.Google Scholar
  17. 17.
    Yu, Y., & Acton, S. T. (2002). Deconvolutional speckle reducing anisotropic diffusion. In International conference on image processing, ICIP, (vol. 11(11), pp. 1260–1270).Google Scholar
  18. 18.
    Tauber, C., Batatia, H., & Ayache, A. (2004). A robust speckle reducing anisotropic diffusion. In International conference on image processing, ICIP, (vol. 1(2), pp. 247–250).Google Scholar
  19. 19.
    Aja-Fernández, S., & Alberola-López, C. (2006). On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Transactions on Image Processing, 15(9), 2694–2701.Google Scholar
  20. 20.
    Krissian, K., Westin, C., Kikinis, R., & Vosburgh, K. G. (2007). Anisotropic diffusion. IEEE Transactions on Image Processing, 16(5), 1412–1424.MathSciNetzbMATHGoogle Scholar
  21. 21.
    Liu, X., Liu, J., Xu, X., Chun, L., Tang, J., & Deng, Y. (2011). A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images. BMC Genomics, 12(SUPPL), 5.Google Scholar
  22. 22.
    Toufique, Y., El Moursli, R. C., Masmoudi, L., El Kharrim, A., Kaci, M., & Allal, S. (2014) Ultrasound image enhancement using an adaptive anisotropic diffusion filter. In Middle east conference on biomedical engineering, (pp. 1–4).Google Scholar
  23. 23.
    Ramos-llordén, G., Vegas-sánchez-ferrero, G., Martin-fernandez, M., Alberola-López, C., & Aja-Fernández, S. (2015). Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Transactions on Image Processing, 24(1), 345–358.MathSciNetGoogle Scholar
  24. 24.
    Hu, Z., & Tang, J. (2016) Cluster driven anisotropic diffusion for speckle reduction in ultrasound images. In International conference on image processing, (pp. 2325–2329).Google Scholar
  25. 25.
    Fredj, A. H., Malek, J., & Bourennane, E. B. (2016). Fast oriented anisotropic diffusion filter. In International design and test workshop, (pp. 308–312).Google Scholar
  26. 26.
    Scherzer, O., & Weickert, J. (2000). Relations between regularization and diffusion filtering. Journal of Mathematical Imaging and Vision, 12(1), 43–63.MathSciNetzbMATHGoogle Scholar
  27. 27.
    Yue, Y., Croitoru, M. M., Bidani, A., Zwischenberger, J. B., & Clark, J. W. (2006). Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images. IEEE Transactions on Medical Imaging, 25(3), 297–311.Google Scholar
  28. 28.
    Wang, Y., & Zhou, H. (2006). Total variation wavelet-based medical image denoising. International Journal of Biomedical Imaging, 2006, 1–12.Google Scholar
  29. 29.
    Bhoi, N., & Meher, S. (2008). Total variation based wavelet domain filter for image denoising. In International conference on emerging trends in engineering and technology, (pp. 20–25).Google Scholar
  30. 30.
    Huang, Y., Ng, M. K., & Wen, Y. (2009). A new total variation method for multiplicative noise removal *. SIAM Journal on Imaging Sciences, 2(1), 20–40.MathSciNetzbMATHGoogle Scholar
  31. 31.
    Bredies, K., Kunisch, K., & Pock, T. (2010). Total generalized variation. SIAM Journal on Imaging Sciences, 3(3), 492–526.MathSciNetzbMATHGoogle Scholar
  32. 32.
    Abrahim, B. A., & Kadah, Y. (2011). Speckle noise reduction method combining total variation and wavelet shrinkage for clinical ultrasound imaging. In middle east conference on biomedical engineering, (pp. 80–83).Google Scholar
  33. 33.
    Jin, Z., & Yang, X. (2011). A variational model to remove the multiplicative noise in ultrasound images. Journal of Mathematical Imaging and Vision, 39(1), 62–74.MathSciNetzbMATHGoogle Scholar
  34. 34.
    Xiaorong, X. U., & Yongjun, L. I. (2013). Image denoising research based on total variation and wavelet transformation. In International conference on consumer electronics, communications and networks, (pp. 339–342).Google Scholar
  35. 35.
    Huang, J., & Yang, X. (2013). Fast reduction of speckle noise in real ultrasound images. Signal Processing, 93(4), 684–694.MathSciNetGoogle Scholar
  36. 36.
    Feng, W., Lei, H., & Gao, Y. (2014). Speckle reduction via higher order. IEEE Transactions on Image Processing, 23(4), 1831–1843.MathSciNetzbMATHGoogle Scholar
  37. 37.
    Elyasi, I., & Pourmina, M. A. (2016). Reduction of speckle noise ultrasound images based on TV regularization and modified bayes shrink techniques. Optik (Stuttg), 127(24), 11732–11744.Google Scholar
  38. 38.
    Wang, S., Huang, T.-Z., Zhao, X.-L., Mei, J.-J., & Huang, J. (2018). Speckle noise removal in ultrasound images by first- and second-order total variation. Numerical Algorithms, 78(2), 513–533.MathSciNetzbMATHGoogle Scholar
  39. 39.
    Mei, J. J., Huang, T. Z., Wang, S., & Le Zhao, X. (2018). Second order total generalized variation for speckle reduction in ultrasound images. Journal of the Franklin Institute, 355(1), 574–595.MathSciNetzbMATHGoogle Scholar
  40. 40.
    Goyal, M., & Sekhon, G. S. (2011). Hybrid threshold technique for speckle noise reduction using wavelets for grey scale images. International Journal of Computer Science and Technology, 2(2), 620–625.Google Scholar
  41. 41.
    Donoho, D. L. (1993). Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data. In Proceedings of symposia in applied mathematics (pp. 173–205).Google Scholar
  42. 42.
    Rodríguez, P. (2013). Total variation regularization algorithms for images corrupted with different noise models: A review. Journal of Electrical and Computer Engineering, 1, 2013.MathSciNetGoogle Scholar
  43. 43.
    Rudin, L. I., Osher, S., & Fatemi, E. (1992). Nonlinear total variation noise removal algorithm. Physica D: Nonlinear Phenomena, 60(1–4), 259–268.MathSciNetzbMATHGoogle Scholar
  44. 44.
    Chambolle, A. (2004). An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision, 20(1–2), 89–97.MathSciNetzbMATHGoogle Scholar
  45. 45.
    Aujol, J. F., Gilboa, G., Chan, T., & Osher, S. (2006). Structure-texture image decomposition-modeling, algorithms, and parameter selection. International Journal of Computer Vision, 67(1), 111–136.zbMATHGoogle Scholar
  46. 46.
    Hassanpour, H., Samadiani, N., & Mahdi Salehi, S. M. (2015). Using morphological transforms to enhance the contrast of medical images. The Egyptian Journal of Radiology and Nuclear Medicine, 46(2), 481–489.Google Scholar
  47. 47.
    Kaur, J., Kaur, J., & Kaur, M. (2011). Survey of despeckling techniques for medical ultrasound images. International Journal of Computer Technology and Applications, 2(4), 1003–1007.Google Scholar
  48. 48.
    Jensen, & Svendsen. (1992). Field II simulation program. [Online]. Available: http://field-ii.dk/examples/ftp_files/. Accessed 05 Oct 2017.
  49. 49.
    Geertsma, T. S. A. (2011). Ultrasound cases. Ultrasoundcases.Info. [Online]. Available: http://www.ultrasoundcases.info/category.aspx?cat=66. Accessed 28 Aug 2017.
  50. 50.
    Martin Zukal, P. D. Ing., Radek Beneš, Ing., Petr Číka, Ing., Kamil Říha, Ing. (2011). Ultrasound image database. [Online]. Available: http://splab.cz/en/download/databaze/ultrasound. Accessed: 29 Aug 2017.
  51. 51.
    Rangaraju, K. S., Kumar, K., & Renumadhavi, C. H. (2012). Review paper on quantitative image quality assessment-medical ultrasound images. International Journal of Engineering, 1(4), 1–6.Google Scholar
  52. 52.
    Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9(3), 81–84.Google Scholar
  53. 53.
    Poobal, S., & Ravindran, G. (2011). The performance of fractal image compression on different imaging modalities using objective quality measures. International Journal of Engineering Science and Technology, 3(1), 525–530.Google Scholar
  54. 54.
    Xu, S., Liu, X., & Jiang, S. (2015). A fast feature similarity index for image quality assessment. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(11), 179–194.Google Scholar
  55. 55.
    Santos, C. A. N., Martins, D. L. N., & Mascarenhas, N. D. A. (2017). Ultrasound Image despeckling using stochastic distance-based BM3D. IEEE Transactions on Image Processing, 26(6), 2632–2643.MathSciNetGoogle Scholar
  56. 56.
    Dellepiane, S. G., & Angiati, E. (2014). Quality assessment of despeckled SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2), 691–707.Google Scholar
  57. 57.
    Nisha, A., & Kumar, S. (2013). Image quality assessment techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 3(7), 636–640.Google Scholar
  58. 58.
    You, Y. L., & Kaveh, M. (2000). Fourth-order partial differential equations for noise removal. IEEE Transactions on Image Processing, 9(10), 1723–1730.MathSciNetzbMATHGoogle Scholar
  59. 59.
    Wang, Y., Chen, W., Zhou, S., Yu, T., & Zhang, Y. (2011). MTV: modified total variation model for image noise removal. IEEE Electronics Letters, 47(10), 592–594.Google Scholar
  60. 60.
    Goldstein, T., & Osher, S. (2009). The split Bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences, 2(2), 323–343.MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSLIETLongowalIndia

Personalised recommendations