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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 31, Iss. 6 — Jun. 1, 2014
  • pp: 1273–1283

Gabor-based anisotropic diffusion for speckle noise reduction in medical ultrasonography

Qi Zhang, Hong Han, Chunhong Ji, Jinhua Yu, Yuanyuan Wang, and Wenping Wang  »View Author Affiliations

JOSA A, Vol. 31, Issue 6, pp. 1273-1283 (2014)

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In ultrasound (US), optical coherence tomography, synthetic aperture radar, and other coherent imaging systems, images are corrupted by multiplicative speckle noise that obscures image interpretation. An anisotropic diffusion (AD) method based on the Gabor transform, named Gabor-based anisotropic diffusion (GAD), is presented to suppress speckle in medical ultrasonography. First, an edge detector using the Gabor transform is proposed to capture directionality of tissue edges and discriminate edges from noise. Then the edge detector is embedded into the partial differential equation of AD to guide the diffusion process and iteratively denoise images. To enhance GAD’s adaptability, parameters controlling diffusion are determined from a fully formed speckle region that is automatically detected. We evaluate the GAD on synthetic US images simulated with three models and clinical images acquired in vivo. Compared with seven existing speckle reduction methods, the GAD is superior to other methods in terms of noise reduction and detail preservation.

© 2014 Optical Society of America

OCIS Codes
(030.6140) Coherence and statistical optics : Speckle
(100.2000) Image processing : Digital image processing
(100.2980) Image processing : Image enhancement
(110.4280) Imaging systems : Noise in imaging systems
(110.7170) Imaging systems : Ultrasound

ToC Category:
Image Processing

Original Manuscript: January 29, 2014
Revised Manuscript: March 26, 2014
Manuscript Accepted: April 18, 2014
Published: May 20, 2014

Virtual Issues
Vol. 9, Iss. 8 Virtual Journal for Biomedical Optics

Qi Zhang, Hong Han, Chunhong Ji, Jinhua Yu, Yuanyuan Wang, and Wenping Wang, "Gabor-based anisotropic diffusion for speckle noise reduction in medical ultrasonography," J. Opt. Soc. Am. A 31, 1273-1283 (2014)

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  1. J. Liu, T.-Z. Huang, Z. Xu, and X.-G. Lv, “High-order total variation-based multiplicative noise removal with spatially adapted parameter selection,” J. Opt. Soc. Am. A 30, 1956–1966 (2013). [CrossRef]
  2. C. Yoon, G.-D. Kim, Y. Yoo, T.-K. Song, and J. H. Chang, “Frequency equalized compounding for effective speckle reduction in medical ultrasound imaging,” Biomed. Signal Process. Control 8, 876–887 (2013). [CrossRef]
  3. Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11, 1260–1270 (2002). [CrossRef]
  4. S. Esakkirajan, C. T. Vimalraj, R. Muhammed, and G. Subramanian, “Adaptive wavelet packet-based de-speckling of ultrasound images with bilateral filter,” Ultrasound Med. Biol. 39, 2463–2476 (2013). [CrossRef]
  5. B. F. Kennedy, A. Curatolo, T. R. Hillman, C. M. Saunders, and D. D. Sampson, “Speckle reduction in optical coherence tomography images using tissue viscoelasticity,” J. Biomed. Opt. 16, 020506 (2011). [CrossRef]
  6. M. Szkulmowski, I. Gorczynska, D. Szlag, M. Sylwestrzak, A. Kowalczyk, and M. Wojtkowski, “Efficient reduction of speckle noise in optical coherence tomography,” Opt. Express 20, 1337–1359 (2012). [CrossRef]
  7. E. Götzinger, M. Pircher, B. Baumann, T. Schmoll, H. Sattmann, R. A. Leitgeb, and C. K. Hitzenberger, “Speckle noise reduction in high speed polarization sensitive spectral domain optical coherence tomography,” Opt. Express 19, 14568–14585 (2011). [CrossRef]
  8. J.-S. Lee, “Speckle analysis and smoothing of synthetic aperture radar images,” Comput. Graph. Image Process. 17, 24–32 (1981). [CrossRef]
  9. K. Ramamoorthy, T. Chelladurai, P. Sundararajan, and M. Krishnamurthy, “Noise suppression using weighted median filter for improved edge analysis in ultrasound kidney images,” Int. J. Comp. Sci. Mobile Comput. 3, 97–105 (2014).
  10. F. Latifoğlu, “A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application,” Comput. Methods Programs Biomed. 111, 561–569 (2013). [CrossRef]
  11. J. Yu, J. Tan, and Y. Wang, “Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method,” Pattern Recogn. 43, 3083–3092 (2010). [CrossRef]
  12. Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).
  13. Y. Gu, Z. Cui, C. Xiu, and L. Wang, “Ultrasound echocardiography despeckling with non-local means time series filter,” Neurocomputing 124, 120–130 (2014). [CrossRef]
  14. Y. Wu, B. Tracey, P. Natarajan, and J. P. Noonan, “James-Stein type center pixel weights for non-local means image denoising,” IEEE Signal Process. Lett. 20, 411–414 (2013). [CrossRef]
  15. A. Buades, B. Coll, and J.-M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul. 4, 490–530 (2005). [CrossRef]
  16. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990). [CrossRef]
  17. F. Zhang, Y. M. Yoo, L. M. Koh, and Y. Kim, “Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction,” IEEE Trans. Med. Imag. 26, 200–211 (2007). [CrossRef]
  18. G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004). [CrossRef]
  19. A. Araújo, S. Barbeiro, and P. Serranho, “Stability of finite difference schemes for complex diffusion processes,” SIAM J. Numer. Anal. 50, 1284–1296 (2012). [CrossRef]
  20. R. Bernardes, C. Maduro, P. Serranho, A. Araújo, S. Barbeiro, and J. Cunha-Vaz, “Improved adaptive complex diffusion despeckling filter,” Opt. Express 18, 24048–24059 (2010). [CrossRef]
  21. J. Jose, A. Prahladan, and M. S. Nair, “Speckle reduction and contrast enhancement of ultrasound images using anisotropic diffusion with Jensen Shannon divergence operator,” Biomed. Eng. Lett. 3, 87–94 (2013). [CrossRef]
  22. Y. Yu and S. T. Acton, “Edge detection in ultrasound imagery using the instantaneous coefficient of variation,” IEEE Trans. Image Process. 13, 1640–1655 (2004). [CrossRef]
  23. C. Lopez-Molina, M. Galar, H. Bustince, and B. De Baets, “On the impact of anisotropic diffusion on edge detection,” Pattern Recogn. 47, 270–281 (2014). [CrossRef]
  24. R. N. Czerwinski, D. L. Jones, and W. D. O’Brien, “Ultrasound speckle reduction by directional median filtering,” in International Conference on Image Processing (IEEE Signal Processing Society, 1995), pp. 358–361.
  25. E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Fast multiscale directional filter bank-based speckle mitigation in gallstone ultrasound images,” J. Opt. Soc. Am. A 31, 283–292 (2014). [CrossRef]
  26. Y. Guo, H. D. Cheng, J. Tian, and Y. Zhang, “A novel approach to speckle reduction in ultrasound imaging,” Ultrasound Med. Biol. 35, 628–640 (2009). [CrossRef]
  27. M. J. Lyons, J. Budynek, and S. Akamatsu, “Automatic classification of single facial images,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1357–1362 (1999). [CrossRef]
  28. C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Trans. Image Process. 11, 467–476 (2002). [CrossRef]
  29. C. Tsiotsios and M. Petrou, “On the choice of the parameters for anisotropic diffusion in image processing,” Pattern Recogn. 46, 1369–1381 (2013). [CrossRef]
  30. J. A. Jensen and N. B. Svendsen, “Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 39, 262–267 (1992). [CrossRef]
  31. J. A. Jensen, “Field: a program for simulating ultrasound systems,” in 10th Nordicbaltic Conference on Biomedical Imaging (Citeseer, 1996), pp. 351–353.
  32. Q. Zhang, Y. Wang, W. Wang, J. Ma, J. Qian, and J. Ge, “Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform,” Ultrasound Med. Biol. 36, 111–129 (2010). [CrossRef]
  33. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004). [CrossRef]
  34. Y. Zhang, H. D. Cheng, J. Huang, and X. Tang, “An effective and objective criterion for evaluating the performance of denoising filters,” Pattern Recogn. 45, 2743–2757 (2012). [CrossRef]

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