A Novel Speckle Reducing Scan Conversion in Ultrasound Imaging System

  • Dipannita Ghosh
  • Debashis Nandi
  • Palash Ghosal
  • Amish Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)


Quality of ultrasound image is dominantly limited by two major issues such as low resolution and speckle noise. The existing speckle reduction techniques are mostly applied either before or after scan conversion. Filtering before scan conversion results in huge computational load since the amount of data handled is quite large while filtering after scan conversion provides poor image quality. In this paper, a novel and computationally efficient filtering technique has been proposed where filtering is performed along with scan conversion using spatial linear adaptive and nonlinear filters in two directions of scan conversion geometry. The proposed framework is found suitable for the real-time applications and improves the visual quality of the image. Quality metrics for the proposed method have been compared to other existing methods to show the novelty of the work.


Ultrasound image Scan conversion Speckle reduction 


  1. 1.
    Goodman, J.W.: Some fundamental properties of speckle. J. Opt. Soc. Am. 66, 1145–1150 (1976)Google Scholar
  2. 2.
    Michailovich, O.V., Tannenbaum, A.: Despeckling of Medical Ultrasound Images. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 53(1), 64–78 (2006)Google Scholar
  3. 3.
    Lee, J.S.: Refined Filtering of Image Noise Using Local Statistics. Computer Graphics and Image Processing. 15(1), 380–389 (1981)Google Scholar
  4. 4.
    Bamber, J.C., Daft, C.: Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images. Ultrasonics. 24(1), 41–43 (1986)Google Scholar
  5. 5.
    Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P.: Adaptive restoration of images with speckle. IEEE Transactions Acoustics. Speech and Signal Processing. 35(3), 373–383 (1987)Google Scholar
  6. 6.
    Loupas, T., McDicken, W.N., Allan, P.L.: An Adaptive Weighted Median Filter for Speckle Suppression in Medical Ultrasonic Images. IEEE Transactions on Circuits and Systems. 36(1), 129–135 (1989)Google Scholar
  7. 7.
    Dutt, V., Greenleaf, J.F.: Adaptive speckle reduction filter for log compressed B-scan images. IEEE Transactions on Medical Imaging. 15(6), 802–813 (1996)Google Scholar
  8. 8.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 4(7), 629–639 (1990)Google Scholar
  9. 9.
    Krissian, K., Fedrij, C.: Oriented Speckle reducing aniosotropic diffusion. IEEE Transactions on Image Processing. 16(5), 1412–1424 (2007)Google Scholar
  10. 10.
    Donoho, D.L.: De-Noising by Soft-Thresholding. IEEE Transactions on Information Theory. 41(3), 613–627 (1995)Google Scholar
  11. 11.
    Gleich, D., Datcu, M.: Wavelet-Based SAR Image Despeckling and Information Extraction Using Particle Filter. IEEE Transactions on Image Processing. 18(10), 2167–2184 (2009)Google Scholar
  12. 12.
    Behar, V., Adam, D., Friedman, Z.: A new method of spatial compounding imaging. Ultrasonics. 41(5), 377–384 (2003)Google Scholar
  13. 13.
    Li, P.C., Chen, M.J.: Strain Compounding: A New Approach for speckle reduction. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 49(1), 39–46 (2002)Google Scholar
  14. 14.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. Computer Vision and Pattern Recognition. CVPR. IEEE computer Society Conference. 2, 60–65 (2005)Google Scholar
  15. 15.
    Coupe, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal Means-Based Speckle Filtering for Ultrasound Images. IEEE Transactions on Image Processing. 18, 2221–2229 (2009)Google Scholar
  16. 16.
    Foucher, S.: SAR image Filtering via learned dictionaries and sparse representations. Geoscience and Remote sensing symposium. IGARSS. IEEE International. 1, I-229–I-232 (2008)Google Scholar
  17. 17.
    Czerwinski, R. N., Jones, D. L., OBrien, Jr. W. D.: Ultrasound Speckle Reduction by Directional Median Filtering’. IEEE Proceedings. International Conference on Image Processing. 1, 358–361 (1995)Google Scholar
  18. 18.
    Gungor, M.A., Karagoz, I.: The homogeneity map method for speckle reduction in diagnostic ultrasound images. Measurement: Journal of the International Measurement Confederations. 68, 100–110 (2015)Google Scholar
  19. 19.
    Wang, Z. and Bovik, A. C.: Image quality Assessment: From Error Visibility to Structure Similarity, IEEE Trans. on Image Processing. 13(4), 600–612 (2004)Google Scholar
  20. 20.
    Crete, F., Dolmiere, T., Ladret P., Nicolas M.: The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric. SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging. San Jose:tats-Unisd’Amrique (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Dipannita Ghosh
    • 1
  • Debashis Nandi
    • 1
  • Palash Ghosal
    • 1
  • Amish Kumar
    • 1
  1. 1.Department of Information TechnologyNational Institute of TechnologyDurgapurIndia

Personalised recommendations