Speckle Noise Reduction in Breast Ultrasound Images for Segmentation of Region Of Interest (ROI) Using Discrete Wavelets

  • S. Amutha
  • D. R. Ramesh Babu
  • R. Mamatha
  • S. Vidhya Suman
  • M. Ravi Shankar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


Ultrasound imaging is a widely used diagnostic technique for the early detection of breast diseases. However, the usefulness of ultrasound imaging is degraded by the multiplicative speckle noise. This reduces the efficiency of diagnosis by radiologists. In order to improve the efficiency of diagnosis, we propose an algorithm for speckle denoising and edge enhancement for the segmentation of ROI. The algorithm is performed in three steps. In the first step, speckle denoising is achieved through shrinkage based on local variance matrix. The second step enhances the edges based on formation of homogenous blocks. The third steps segments the object boundaries based on K-means clustering algorithm. The results of the proposed method have been compared with the well known filters. The experimental results show that the proposed algorithm has considerably improved the image quality without providing any noticeable artifact.


Breast ultrasound Speckle noise Edge enhancement Segmentation of ROI Wavelets 



The authors would like to thank Dr. Sairam Geethanath, HOD, Department of Medical Electronics, Dayanand Sagar College of Engineering, India for his support for the development of this work.


  1. 1.
    American Cancer Society Breast Cancer Facts and Figures (2012). Atlanta, USGoogle Scholar
  2. 2.
    Joseph YL, Carey EF (1999) Application of artificial neural networks for diagnosis of breast cancer. In: Proceedings of the congress of evolutionary computation, Washington, USA, 1755–1759Google Scholar
  3. 3.
    Kopans DB (1992) The positive predictive value of mammography. Am J Roentgenol 158(3):521–526CrossRefGoogle Scholar
  4. 4.
    Knutzen AM, Gisvold JJ (1993) Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clinic Proc 68(5):454–460Google Scholar
  5. 5.
    Hoboken A (2003) Webb introduction to biomedical imaging. Wiley, New York, USAGoogle Scholar
  6. 6.
    Jain AK (1989) Fundamental of digital image processing. Prentice-Hall, NJGoogle Scholar
  7. 7.
    Narayanan SK, Wahidabanu RSD (2009) A view on despeckling in ultrasound imaging. Int J Signal Process Image Process Pattern Recogn 2(3):85–98Google Scholar
  8. 8.
    Kaun DT, Sawchuk TC, Chavel SP (1987) Adoptive restoration of images with speckle. IEEE Trans Acoust Speech Signal Process. vol. ASSP-35Google Scholar
  9. 9.
    Frost VS, Stiles JA, Shanmugan KS, Hltzman JC (1982) A model for radar images and its application to adoptive digital filtering for multiplicative noise. IEEE Trans Pattern Anal Mach Intell. vol. PAMI-4Google Scholar
  10. 10.
    Yu Y, Acton ST (2002) Speckle reducing anisotropic diffijsion. IEEE Trans Image Process, 11(11) Nov 2002Google Scholar
  11. 11.
    Gupta N, Swamy MNS, Plotkin E (2005) Despeckling ofi medical ultrasoundi mages using data and rate adoptive lossy compression. IEEE Trans Med Imag 24(6) Jun 2005Google Scholar
  12. 12.
    Kim YS, Ra JB (2005) Improvement of ultrasound image based on wavelet transform: speckle reduction and edge enhancement. In: Proceedings of the SPIE Vol. 5747 (SPIE, Bellingham, WA)Google Scholar
  13. 13.
    Zhou Q, Liu L, Zhang D, Bian Z (2002) Denoise and contrast enhancement of ultrasound speckle image based on wavelet. In: Proceedings of the ICSP p 1500–1503Google Scholar
  14. 14.
    Xu Y, Nishimura T (2009) Segmentation of breast lesions in ultrasound images using spatial fuzzy clustering and structure Tensors. World Academy of Science, Engineering and Technology 53, Kitakyushu-shi, JapanGoogle Scholar
  15. 15.
    Wang Y-J, Lu S-X (2009) Breast ultrasound images enh ancement using fuzzy logic. doi  10.1109/DBTA.2009.90. Washington, USA
  16. 16.
    Tiana J-W, Wang Y, Huang J-H, Ning C-P, Wang.H-M, Liu Y, Tang X-L (2008) The digital database for breast ultrasound image. In: Proceedings of the 11th joint conference on information sciences, Harbin, ChinaGoogle Scholar
  17. 17.
    Banazier AA, Kadah Y (2011) Speckle noise reduction method combining total variation and wavelet shrinkage for clinical ultrasound imaging. 978-1-4244-7000-6/11 © 2011 IEEE. Biomedical engineering department Cairo University Cairo, EgyptGoogle Scholar
  18. 18.
    Xinwu LI (2008) A volume segmentation algorithm for medical image based on K-means clustering. Int Conf Intell Inf Hiding Multimedia Sig Proc. 978-0-7695-3278-3/08 © 2008 IEEE doi  10.1109/IIH- MSP.2008.161

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • S. Amutha
    • 1
  • D. R. Ramesh Babu
    • 1
  • R. Mamatha
    • 1
  • S. Vidhya Suman
    • 1
  • M. Ravi Shankar
    • 1
  1. 1.Department of Computer ScienceDayananda Sagar College of EngineeringBangaloreIndia

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