Scatterer Density in Edge and Coherence Enhancing Nonlinear Anisotropic Diffusion for Medical Ultrasound Speckle Reduction
This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
[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] V. Dutt, "Statistical analysis of ultrasound echo envelope," Ph.D.
dissertation, Mayo Graduate School, Rochester, MN, 1995.
[5] D. Blacknell, "Comparison of parameter estimators for K-distribution,"
IEE Proceedings - Radar, Sonar Navigation, vol. 141, pp. 45-52, Feb.
1994.
[6] J. C. Bamber and G. Cook-Martin, "Texture analysis and speckle
reduction in medical echography," SPIE, pp. 120-127, 1987.
[7] 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.
[8] 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.
[9] 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.
[10] J.Weickert, "Mutiscale texture enhancement," in Lecture Notes in.
Computer Science, ser. Berlin, vol. 970, Computer Analysis of Images
and Patterns, pp. 230-237, 1995.
[11] J. Weickert et al., "Efficient and reliable schemes for nonlinear diffusion
filtering," IEEE Trans. Imag. Processing, vol. 7, pp. 398-410, Mar.
1998.
[12] 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, 2000.
[13] 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.
[14] 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.
[15] R. A. Carmona and S. Zhong, "Adaptive smoothing respecting feature
directions," IEEE Trans. Image Processing, vol. 7, pp. 353-8, Mar.
1998.
[16] 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.
[17] 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.
[18] 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.
[19] A.N. Netravali and B.G. Haskell, Digital pictures: representation,
compression, and standards (2nd Ed), Plenum Press, New York, NY
(1995).
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] Y. Yu and T. Acton, "Speckle reducing anisotropic diffusion," IEEE
Trans. On Image Processing, vol. 11, No. 11, pp. 1260-1270, November
2002.
[27] A. Badawi, "Scatterer density in nonlinear diffusion for speckle
reduction in ultrasound imaging: the isotropic case," International
Journal of Biomedical Sciences, vol. 2, No. 3, 2007.
[28] 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.
[29] 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.
[30] 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.
[31] M. Rabbani and P.W. Jones, Digital image compression techniques, vol.
TT7, SPIE Optical Engineering Press, Bellvue, Washington (1991).
[32] 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.
[33] R. V. D. Boomgaard, Algorithms for non-linear diffusion Matlab in a
literate programming style, Intelligent Sensory Information Systems,
University of Amsterdam, The Netherlands
http://carol.wins.uva.nl/~rein/nldiffusionweb/material.html (Accessed
Feb. 2007).
[34] W. K. Pratt, Digital Image Processing, Wiley, New York, NY, USA,
1978.
[35] J. Weickert, "Anisotropic Diffusion in Image Processing," PhD
dissertation, Kaiserslautern University, Germany, 1996.
[36] J. Weickert, "Coherence-Enhancing Diffusion Filtering," International
Journal of Computer Vision, vol. 31(2/3), pp 111-127, 1999.
[37] J Weickert. "A Scheme for Coherence-Enhancing Diffusion Filtering
with Optimized Rotation Invariance," Journal of Visual Communication
and Image Representation, vol. 13, pp 103-118, 2002.
[38] T. Loupas, W. N. Mcdicken, and P. L. Allan, "An adaptive weighted
median filter for speckle suppression in medical ultrasonic images,"
IEEE Trans. Circuits Syst., vol. 36, no. 1, pp. 129-135, Jan. 1989.
[39] 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.
[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] V. Dutt, "Statistical analysis of ultrasound echo envelope," Ph.D.
dissertation, Mayo Graduate School, Rochester, MN, 1995.
[5] D. Blacknell, "Comparison of parameter estimators for K-distribution,"
IEE Proceedings - Radar, Sonar Navigation, vol. 141, pp. 45-52, Feb.
1994.
[6] J. C. Bamber and G. Cook-Martin, "Texture analysis and speckle
reduction in medical echography," SPIE, pp. 120-127, 1987.
[7] 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.
[8] 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.
[9] 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.
[10] J.Weickert, "Mutiscale texture enhancement," in Lecture Notes in.
Computer Science, ser. Berlin, vol. 970, Computer Analysis of Images
and Patterns, pp. 230-237, 1995.
[11] J. Weickert et al., "Efficient and reliable schemes for nonlinear diffusion
filtering," IEEE Trans. Imag. Processing, vol. 7, pp. 398-410, Mar.
1998.
[12] 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, 2000.
[13] 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.
[14] 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.
[15] R. A. Carmona and S. Zhong, "Adaptive smoothing respecting feature
directions," IEEE Trans. Image Processing, vol. 7, pp. 353-8, Mar.
1998.
[16] 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.
[17] 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.
[18] 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.
[19] A.N. Netravali and B.G. Haskell, Digital pictures: representation,
compression, and standards (2nd Ed), Plenum Press, New York, NY
(1995).
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] Y. Yu and T. Acton, "Speckle reducing anisotropic diffusion," IEEE
Trans. On Image Processing, vol. 11, No. 11, pp. 1260-1270, November
2002.
[27] A. Badawi, "Scatterer density in nonlinear diffusion for speckle
reduction in ultrasound imaging: the isotropic case," International
Journal of Biomedical Sciences, vol. 2, No. 3, 2007.
[28] 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.
[29] 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.
[30] 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.
[31] M. Rabbani and P.W. Jones, Digital image compression techniques, vol.
TT7, SPIE Optical Engineering Press, Bellvue, Washington (1991).
[32] 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.
[33] R. V. D. Boomgaard, Algorithms for non-linear diffusion Matlab in a
literate programming style, Intelligent Sensory Information Systems,
University of Amsterdam, The Netherlands
http://carol.wins.uva.nl/~rein/nldiffusionweb/material.html (Accessed
Feb. 2007).
[34] W. K. Pratt, Digital Image Processing, Wiley, New York, NY, USA,
1978.
[35] J. Weickert, "Anisotropic Diffusion in Image Processing," PhD
dissertation, Kaiserslautern University, Germany, 1996.
[36] J. Weickert, "Coherence-Enhancing Diffusion Filtering," International
Journal of Computer Vision, vol. 31(2/3), pp 111-127, 1999.
[37] J Weickert. "A Scheme for Coherence-Enhancing Diffusion Filtering
with Optimized Rotation Invariance," Journal of Visual Communication
and Image Representation, vol. 13, pp 103-118, 2002.
[38] T. Loupas, W. N. Mcdicken, and P. L. Allan, "An adaptive weighted
median filter for speckle suppression in medical ultrasonic images,"
IEEE Trans. Circuits Syst., vol. 36, no. 1, pp. 129-135, Jan. 1989.
[39] 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.
@article{"International Journal of Medical, Medicine and Health Sciences:58510", author = "Ahmed Badawi and J. Michael Johnson and Mohamed Mahfouz", title = "Scatterer Density in Edge and Coherence Enhancing Nonlinear Anisotropic Diffusion for Medical Ultrasound Speckle Reduction", abstract = "This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.", keywords = "Nonlinear anisotropic diffusion, ultrasound imaging, speckle reduction, scatterer density estimation, edge based enhancement, coherence enhancement.", volume = "1", number = "5", pages = "251-24", }
