A maximum likelihood filter using non-local information for despeckling of ultrasound images

Original Paper
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Abstract

This work presents a new ultrasound image despeckling method based on the maximum likelihood principle that effectively exploits non-local information for estimating noise-free pixels. First, a new maximum likelihood filter is proposed which uses neighborhood information to despeckle images. For this purpose, the general speckle model is used in the log-likelihood function and despeckled pixels are obtained by maximizing this function. Second, the proposed filter is developed to use non-local information such that the distribution of each noisy pixel is weighted according to the statistical distance between the patch of the noisy pixel and that of the target pixel. Because it is optimally designed for ultrasound images, the Pearson distance is used to measure the statistical distance between the patches. A series of experiments are conducted on three different ultrasound images and one synthetic image. Subjective evaluations show that the proposed method is able to preserve edges and structural details of the image and objective evaluations using equivalent number of looks, natural image quality evaluator, peak signal-to-noise ratio, mean preservation, and structural similarity confirm that the proposed method can achieve superior performance.

Keywords

Ultrasound despeckling Maximum likelihood filter Non-local information Speckle noise 

References

  1. 1.
    Mittal, D., Kumar, V., Saxena, S.C., Khandelwal, N., Kalra, N.: Enhancement of the ultrasound images by modified anisotropic diffusion method. Med. Biol. Eng. Comput. 48(12), 1281–1291 (2010)CrossRefGoogle Scholar
  2. 2.
    Gupta, D., Anand, R.S., Tyagi, B.: Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images. Biomed. Signal Process. Control 10, 79–91 (2014)CrossRefGoogle Scholar
  3. 3.
    Sudeep, P.V., Palanisamy, P., Rajan, J., Baradaran, H., Saba, L., Gupta, A., Suri, J.S.: Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomed. Signal Process. Control 28, 1–8 (2016)CrossRefGoogle Scholar
  4. 4.
    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)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Shahdoosti, H.R., Hazavei, S.M.: Image denoising in dual contourlet domain using hidden Markov tree models. Digit. Signal Proc. 67, 17–29 (2017)CrossRefGoogle Scholar
  6. 6.
    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)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2, 165–168 (1980)CrossRefGoogle Scholar
  8. 8.
    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, 157–166 (1982)CrossRefGoogle Scholar
  9. 9.
    Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P.: Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. 2, 165–177 (1985)CrossRefGoogle Scholar
  10. 10.
    Lopes, A., Touzi, R., Nezry, E.: Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sens. 28(6), 992–1000 (1990)CrossRefGoogle Scholar
  11. 11.
    Qiu, F., Berglund, J., Jensen, J.R., Thakkar, P., Ren, D.: Speckle noise reduction in SAR imagery using a local adaptive median filter. GISci. Remote Sens. 41(3), 244–266 (2004)CrossRefGoogle Scholar
  12. 12.
    Vanithamani, R., Umamaheswari, G., Ezhilarasi, M.: Modified hybrid median filter for effective speckle reduction in ultrasound images. In: Recent Advances in Networking, VLSI and Signal Processing, pp. 166–171 (2010)Google Scholar
  13. 13.
    Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Aja-Fernández, S., Alberola-López, C.: On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image Process. 15(9), 2694–2701 (2006)CrossRefGoogle Scholar
  15. 15.
    Vegas-Sánchez-Ferrero, G., Aja-Fernández, S., Martín-Fernández, M., Frangi, A.F., Palencia, C.: Probabilistic-driven oriented speckle reducing anisotropic diffusion with application to cardiac ultrasonic images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 518–525. Springer, Berlin (2010)Google Scholar
  16. 16.
    Bini, A.A., Bhat, M.S.: Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion. Multidimens. Syst. Signal Process. 25(1), 41–65 (2014)CrossRefMATHGoogle Scholar
  17. 17.
    Shao, D., Zhou, T., Liu, F., Yi, S., Xiang, Y., Ma, L., Xiong, X., He, J.: Ultrasound speckle reduction based on fractional order differentiation. J. Med. Ultrason. 44(3), 227–237 (2017)CrossRefGoogle Scholar
  18. 18.
    Wang, S., Huang, T.Z., Zhao, X.L., Mei, J.J., Huang, J.: Speckle noise removal in ultrasound images by first-and second-order total variation. Numer. Algorithms 1–21 (2017)Google Scholar
  19. 19.
    Singh, C., Ranade, S.K., Singh, K.: Invariant moments and transform-based unbiased nonlocal means for denoising of MR images. Biomed. Signal Process. Control 30, 13–24 (2016)CrossRefGoogle Scholar
  20. 20.
    Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: May. Bayesian non local means-based speckle filtering. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008, pp. 1291–1294. IEEE (2008)Google Scholar
  21. 21.
    Baselice, F.: Ultrasound image despeckling based on statistical similarity. Ultrasound Med. Biol. 43(9), 2065–2078 (2017)CrossRefGoogle Scholar
  22. 22.
    Ni, W., Gao, X.: Despeckling of SAR image using generalized guided filter with Bayesian nonlocal means. IEEE Trans. Geosci. Remote Sens. 54(1), 567–579 (2016)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Mingliang, X., Pei, L., Mingyuan, L., Hao, F., Hongling, Z., Bing, Z., Yusong, L., Liwei, Z.: Medical image denoising by parallel non-local means. Neurocomputing 195, 117–122 (2016)CrossRefGoogle Scholar
  24. 24.
    Wang, G., Xu, J., Pan, Z., Diao, Z.: Ultrasound image denoising using backward diffusion and framelet regularization. Biomed. Signal Process. Control 13, 212–217 (2014)CrossRefGoogle Scholar
  25. 25.
    Gupta, D., Anand, R.S., Tyagi, B.: Despeckling of ultrasound medical images using nonlinear adaptive anisotropic diffusion in nonsubsampled shearlet domain. Biomed. Signal Process. Control 14, 55–65 (2014)CrossRefGoogle Scholar
  26. 26.
    Fu, X., Wang, Y., Chen, L., Tian, J.: An image despeckling approach using quantum-inspired statistics in dual-tree complex wavelet domain. Biomed. Signal Process. Control 18, 30–35 (2015)CrossRefGoogle Scholar
  27. 27.
    Singh, K., Ranade, S.K., Singh, C.: A hybrid algorithm for speckle noise reduction of ultrasound images. Comput. Methods Programs Biomed. 148, 55–69 (2017)CrossRefGoogle Scholar
  28. 28.
    Loupas, T., McDicken, W.N., Allan, P.L.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Circuits Syst. 36(1), 129–135 (1989)CrossRefGoogle Scholar
  29. 29.
    Wagner, R.F., Smith, S.W., Sandrik, J.M., Lopez, H.: Statistics of speckle in ultrasound B-scans. IEEE Trans. Sonics Ultrasonics 30(3), 156–163 (1983)CrossRefGoogle Scholar
  30. 30.
    Kumar, B.S.: Image denoising based on non-local means filter and its method noise thresholding. SIViP 7(6), 1211–1227 (2013)CrossRefGoogle Scholar
  31. 31.
    Shahdoosti, H.R., Hazavei, S.M.: Combined ripplet and total variation image denoising methods using twin support vector machines. Multimed. Tools Appl. 77(6), 7013–7031 (2018)CrossRefGoogle Scholar
  32. 32.
    Shahdoosti, H.R.: Two-stage image denoising considering interscale and intrascale dependencies. J. Electron. Imaging 26(6), 063029 (2017)MathSciNetGoogle Scholar
  33. 33.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar
  34. 34.
    Bhuiyan, M.I.H., Ahmad, M.O., Swamy, M.N.S.: Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images. IET Image Proc. 3(3), 147–162 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringHamedan University of TechnologyHamedanIran

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