Scatterer Density in Nonlinear Diffusion for Speckle Reduction in Ultrasound Imaging: The Isotropic Case

This paper proposes a method for speckle reduction in medical ultrasound imaging while preserving the edges with the added advantages of adaptive noise filtering and speed. A nonlinear image diffusion method that incorporates local image parameter, namely, scatterer density in addition to gradient, to weight the nonlinear diffusion process, is proposed. The method was tested for the isotropic case with a contrast detail phantom and varieties of clinical ultrasound images, and then compared to linear and some other diffusion enhancement methods. Different diffusion parameters were tested and tuned to best reduce speckle noise and preserve edges. The method showed superior performance measured both quantitatively and qualitatively when incorporating scatterer density into the diffusivity function. The proposed filter can be used as a preprocessing step for ultrasound image enhancement before applying automatic segmentation, automatic volumetric calculations, or 3D ultrasound volume rendering.

Authors:

• Ahmed Badawi

Keywords:

• Ultrasound imaging
• Nonlinear isotropic diffusion
• Speckle noise
• Scattering.

References:

[1] M. E. Anderson, and G. E. Trahey, "A seminar on k-space applied to
medical ultrasound", http://dukemil.egr.duke.edu/Ultrasound/k-space/
(Accessed July 1, 2006).
[2] J. C. Bamber and C. Daft, "Adaptive filtering for reduction of speckle in
ultrasound pulse-echo images," Ultrasonics, pp. 41-44, Jan. 1986.
[3] T. Iwai, T. Asakura, "Speckle reduction in coherent information
processing", Proceedings of the IEEE, vol. 84, no. 5, 1996.
[4] R. M. Haralick, R. Shanmugam, I. Dinstein, "Textural features for image
classification," IEEE Trans. on Systems, Man, and Cybernetics, 3 (1973)
610-621.
[5] V. Dutt, "Statistical analysis of ultrasound echo envelope," Ph.D.
dissertation, Mayo Graduate School, Rochester, MN, 1995.
[6] D. Blacknell, "Comparison of parameter estimators for K-distribution,"
IEE Proceedings - Radar, Sonar Navigation, vol. 141, pp. 45-52, Feb.
1994.
[7] J. C. Bamber and G. Cook-Martin, "Texture analysis and speckle
reduction in medical echography", SPIE, pp. 120-127, 1987.
[8] Perona, P. and J. Malik, "Scale-space and edge detection using
anisotropic diffusion", Proceedings, IEEE Computer Society workshop
on Computer Vision, 1987, pp. 16-27.
[9] P. Perona and J. Malik, "Scale space and edge detection using
anisotropic diffusion," IEEE Trans. Pattern Anal. Machine Intell., vol.
12, pp. 629-639, July 1990.
[10] M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, "Robust
anisotropic diffusion," IEEE Trans. Imag. Processing, vol. 7, pp. 412-
432, Mar. 1998.
[11] J. Weickert, "Mutiscale texture enhancement," in Lecture Notes in.
Computer Science, ser. Berlin, 1995, vol. 970, Computer Analysis of
Images and Patterns, pp. 230-237.
[12] J. Weickert et al., "Efficient and reliable schemes for nonlinear diffusion
filtering," IEEE Trans. Imag. Processing, vol. 7, pp. 398-410, Mar.
1998.
[13] K. Z. Abd-Elmoniem, Y. M. Kadah, and A. M. Youssef, "Real time
adaptive ultrasound speckle reduction and coherence enhancement,"
presented at the Int. Conf. Imag. Processing (ICIP- 2000), Vancouver,
Canada.
[14] K.Z. Abd-Elmoniem, A. M. Youssef, and Y. M. Kadah, "Real-time
speckle reduction and coherence enhancement in ultrasound imaging
via nonlinear anisotropic diffusion", IEEE Transaction on Biomedical
Engineering, vol. 49, no. 9, pp. 997-1014, 2002.
[15] A. Achim, A. Bezerianos, and P. Tsakalides, "Novel Baysian multiscale
method for speckle removal in medical ultrasound images," IEEE Trans.
Medical Imaging, vol. 20, pp. 772-783, Aug. 2001.
[16] R. A. Carmona and S. Zhong, "Adaptive smoothing respecting feature
directions," IEEE Trans. Image Processing, vol. 7, pp. 353-8, Mar.
1998.
[17] Weeratunga S.K. and C. Kamath, "PDE-based non-linear diffusion
techniques for denoising scientific/industrial images: An empirical
study," Proceedings, Image Processing: Algorithms and Systems, SPIE
Electronic Imaging, pp. 279-290, San Jose, January 2002.
[18] Weeratunga S.K., and C. Kamath, "A comparison of PDE-based nonlinear
anisotropic diffusion techniques for image denoising,"
Proceedings, Image Processing: Algorithms and Systems II, SPIE
Electronic Imaging, San Jose, January 2003.
[19] P. M. Shankar, "A general statistical model for ultrasonic backscattering
from tissues," IEEE Trans. Ultrason. Ferroelect. Freq. Contr., vol. 47,
pp. 727-736, Mar. 2000.
[20] A. M. Badawi, A. M. Hashem, A. S. Derbala, A. Hendawi, A. M.
Youssef, M. F. Abdelwahab, "Ultrasonographic tissue signature for
shistosomal liver and other related liver and other related liver
pathologies", In Proceedings of IEEE Ultrasonics, Ferroelectrics &
Frequency Control Symposium 1995.
[21] S. M. Emara, A. M. Badawi, Abou-Bakr M. Youssef, "Fuzzy Similarity
Measures for Ultrasound Tissue Characterization ", In Proceedings of
Nonlinear Image Processing, SPIE, 1995.
[22] Y. M. Kadah, Aly. A. Farag, Jacek M. Zurada, Ahmed M. Badawi, and
Abou-Bakr M. Youssef, "Classification algorithms for quantitative tissue
characterization of diffuse liver disease from ultrasound images," IEEE
Trans. Medical Imaging, vol. 15, no. 4, pp. 466-478, August 1996.
[23] A. M. Badawi, Ahmed S. Derbala and Abou-Bakr M. Youssef, "Fuzzy
logic algorithm for quantitative tissue characterization of diffuse liver
diseases from ultrasound images," International Journal of Medical
Informatics 55-2 pp. 135-147, 1999.
[24] A.N. Netravali and B.G. Haskell, Digital pictures: representation,
compression, and standards (2nd Ed), Plenum Press, New York, NY
(1995).
[25] D. Clausi and M.E. Jernigan, "A fast method to determine co-occurrence
texture features," IEEE Trans. Geosci. Remote Sensing, vol. 36, pp. 298-
300, Feb. 1998.
[26] D. Clausi and Y. Zhao, "An advanced computational method to
determine co-occurrence probability texture features", Geoscience and
Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE
International, vol. 4, pp. 2453 - 2455, 2002.
[27] J. Weickert, "Applications of nonlinear diffusion in image processing
and computer vision",Acta Math. Univ. Comenianae, Vol. 70(1) (2001),
pp. 33-50.
[28] G. Gilboa, Y.Y. Zeevi, N. Sochen," Image enhancement segmentation
and denoising by time dependent nonlinear diffusion processes",
Proceedings. 2001 International Conference on Image Processing, vol.
3, pp. 134 - 137, Oct. 2001.
[29] R. Touzi, "A review of speckle filtering in the context of estimation
theory," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2392-
2404, Nov. 2002.
[30] J. Lee, "Digital image enhancement and noise filtering using local
statistics," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no. 2,
pp. 165-168, Mar. 1980.
[31] V. Frost, J. Stiles, K. Shanmugan, and J. Holzman, "A model for radar
images and its application to adaptive digital filtering of multiplicative
noise," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-4, no. 2, pp.
157-166, Mar. 1982.
[32] D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive
noise smoothing filter for images with signal-dependent noise," IEEE
Trans. Pattern Anal. Mach. Intell., vol. PAMI-7, no. 2, pp. 165-177,
Mar. 1985.
[33] D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive
restoration of images with speckle," IEEE Trans. Acoust., Speech, Signal
Process., vol. ASSP-35, no. 3, pp. 373-383, Mar. 1987.
[34] Y. Yu and T. Acton, "Speckle reducing anisotropic diffusion", IEEE
Trans. On Image Processing, vol. 11, No. 11, pp. 1260-1270, November
2002.
[35] S. Aja-Fernandez and C. Alberola-lopez, "On the estimation of the
coefficient of variation for anisotropic diffusion speckle filtering", IEEE
Trans. On Image Processing, vol. 15, No. 9, pp. 2694-2701, September
2006.
[36] X. Hao, C. Bruce, C. Pislaru, and J. Greenleaf, "Identification of
reperfused infracted myocardium from high frequency intracardiac
ultrasound images using homodyned K distribution", IEEE Ultrasonics
Symposium, pp. 1189-1192, 2001.
[37] X. Hao, C. Bruce, C. Pislaru, and J. Greenleaf, "Characterization of
reperfused infracted myocardium from high-frequency intracardiac
ultrasound images using homodyned K distribution", IEEE Transaction
on Ultrasonics, Ferroelectric, and Frequency Control, vol. 49, no. 11,
pp. 1530-1542, November 2002.
[38] R. Smolikova, M. Wachowiak, G. Tourassi, and J. Zurada "Neural
estimation of scatterer density in ultrasound", Proceedings of the 2002
International Joint Conference on Neural Networks, vol. 2, pp. 1696 -
1701, 12-17 May 2002.
[39] M. Rabbani and P.W. Jones, Digital image compression techniques, vol.
TT7, SPIE Optical Engineering Press, Bellvue, Washington (1991).
[40] Z. Yang and M. Fox, "Speckle reduction and structure enhancement by
multichannel median boosted anisotropic diffusion", EUROASIP
Journal on Applied Signal Processing, vol. 16, pp. 2492-2502, 2004.
[41] Z. Wang and A. C. Bovik, "A universal image quality index," IEEE
Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.
[42] W. K. Pratt, Digital Image Processing, Wiley, New York, NY, USA,
1978.
[43] R.F. Wagner, M.F. Insana, "Analysis of ultrasound image texture via
generalized Rician statistics," Proc. SPIE 556, pp. 153-159, 1985.
[44] P.M. Shankar, "A general statistical model for ultrasonic backscattering
from tissues," IEEE Trans. Ultrasonics Ferroelectrics and Freq.
Control, vol. 47, no. 3, pp. 727-736, 2000.