Suppression of Speckle Noise in Ultrasound Images Using Bilateral Filter

  • Ananya Gupta
  • Vikrant BhatejaEmail author
  • Avantika Srivastava
  • Aditi Gupta
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)


The suppression of speckle noise is necessary for clear vision of ultrasound images. The quality of ultrasound images is degraded by the presence of speckle noise. In this work, bilateral Filter is used to suppress speckle noise. Conventionally, this filter is used to suppress the Gaussian noise from the images. A bilateral filter is better at edge preserving, noise suppression and for better smoothening of gray as well as colored images. Bilateral filter tends to improve image quality as it replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. These weights are basically based on Gaussian distribution function. The three parameters have been used to analyze the performance of bilateral filter the are PSNR, SSIM, and SSI.


Speckle suppression Bilateral filter PSNR SSIM SSI 


  1. 1.
    Bhateja, V., Tripathi, A., Gupta, A., Lay-Ekuakille, A.: Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring. Measurement 74, 246–254 (2015)CrossRefGoogle Scholar
  2. 2.
    Bhateja, V., Singh, G., Srivastava, A., Singh, J.: Speckle reduction in ultrasound images using an improved conductance function based on anisotropic diffusion. In: Proceedings of International Conference of Computing for Sustainable Global Development (INDIACom), pp. 619—624. IEEE (March, 2014)Google Scholar
  3. 3.
    Bhateja, V., Tiwari, H., Srivastava, A.: A non local means filtering algorithm for restoration of Rician distributed MRI. In: Emerging ICT for Bridging the Future-Proceeding of the 49th Annual Convention of the Computer Society of India CSI, vol. 2, pp. 1–8. Springer, Cham (2015)Google Scholar
  4. 4.
    Zhang, P., Li, F.: A new adaptive weighted mean filter for removing salt and pepper noise. IEEE J. Signal Process. Lett. 21(10), 1280–1283 (2014)Google Scholar
  5. 5.
    Loupas, T., McDicken, N.W., Allan, L.P.: An adaptive weighted median filter for speckle suppression in medical ultrasound images. IEEE Trans. Circuits Syst. 36(1), 129–135 (1989)Google Scholar
  6. 6.
    Lee, S.J.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2(2), 165–168 (1980)Google Scholar
  7. 7.
    Loizou, P.C., Pattichis, S.C., Christodoulou, I.C., Istepanian, S.R., Pantziaris, M., Nicolaides, A.: Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. 52(10), 1653–1669 (2005)Google Scholar
  8. 8.
    Finn, S., Glavin, M., Jones, E.: Echocardiographic speckle reduction comparison. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58(1), 82—101 (2011)Google Scholar
  9. 9.
    Sivakumar, J., Thangavel, K., Saravanan, P.: Computed radiography skull image, enhancement using Wiener filter. In: Proceedings of International Conference on Pattern Recognition, Informatics and Medical Engineering, Henan, China, pp. 307–311. IEEE (2012)Google Scholar
  10. 10.
    Tripathi, A., Bhateja, V., Sharma, A.: Kuan modified anisotropic diffusion approach for speckle filtering. In: Proceedings of the First International Conference on Intelligent Computing and Communication, pp. 537—545. Springer, Singapore (2017)Google Scholar
  11. 11.
    Bhateja, V., Sharma, A., Tripathi, A., Satapathy, S.C., Le, D.N.: An optimized anisotropic diffusion approach for despeckling of SAR images. In: Digital Connectivity Social Impact, pp. 134—140. Springer, Singapore (2016)Google Scholar
  12. 12.
    Baudes, A., Coll, B., Morel, J.: Neighborhood filters and PDE’s. Technical report (2005–04)Google Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Bai, J., Feng, X.: Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process. 16(9) (2007)Google Scholar
  15. 15.
    Dabov, K., Foi, A., Katkovink, V.: Image denoising by sparse 3-D transform domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tomasi, C., Manduchi, R.: Bilateral filter for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1–8. IEEE, Bombay, India (1988)Google Scholar
  17. 17.
    Anh, N.D.: Image denoising by adaptive non-local bilateral filter. IEEE Int. J. Comput. Appl. 99(12) (2014)Google Scholar
  18. 18.
    Bhateja, V., Mishra, M., Urooj, S., Lay-Ekuakille, A.: Bilateral despeckling filter in homogeneity domain for breast ultrasound images. In: Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1027–1032, IEEE (2014)Google Scholar
  19. 19.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ananya Gupta
    • 1
  • Vikrant Bhateja
    • 1
    Email author
  • Avantika Srivastava
    • 1
  • Aditi Gupta
    • 1
  1. 1.Department of Electronics and Communication EngineeringShri Ramswaroop Memorial Group of Professional Colleges (SRMGPC)LucknowIndia

Personalised recommendations