Abstract

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

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    [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]

2014 (4)

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).

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]

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]

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]

2013 (7)

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]

C. Tsiotsios and M. Petrou, “On the choice of the parameters for anisotropic diffusion in image processing,” Pattern Recogn. 46, 1369–1381 (2013).
[CrossRef]

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]

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]

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]

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]

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]

2012 (3)

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]

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]

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]

2011 (2)

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]

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]

2010 (3)

J. Yu, J. Tan, and Y. Wang, “Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method,” Pattern Recogn. 43, 3083–3092 (2010).
[CrossRef]

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]

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]

2009 (1)

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]

2007 (1)

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]

2005 (1)

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]

2004 (3)

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]

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]

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]

2002 (2)

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]

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11, 1260–1270 (2002).
[CrossRef]

1999 (1)

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]

1992 (1)

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]

1990 (1)

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).
[CrossRef]

1981 (1)

J.-S. Lee, “Speckle analysis and smoothing of synthetic aperture radar images,” Comput. Graph. Image Process. 17, 24–32 (1981).
[CrossRef]

Acton, S. T.

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]

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11, 1260–1270 (2002).
[CrossRef]

Akamatsu, S.

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]

Araújo, A.

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]

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]

Barbeiro, S.

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]

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]

Baumann, B.

Bernardes, R.

Bovik, A. C.

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]

Buades, A.

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]

Budynek, J.

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]

Bustince, H.

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]

Chang, J. H.

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]

Chelladurai, T.

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).

Cheng, H. D.

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]

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]

Coll, B.

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]

Cui, Z.

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]

Cunha-Vaz, J.

Curatolo, A.

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]

Czerwinski, R. N.

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.

De Baets, B.

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]

Ding, M.

Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).

Esakkirajan, S.

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]

Galar, M.

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]

Ge, J.

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]

Gilboa, G.

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]

Gorczynska, I.

Götzinger, E.

Gu, Y.

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]

Guo, Y.

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]

Hillman, T. R.

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]

Hitzenberger, C. K.

Huang, J.

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]

Huang, T.-Z.

Jensen, J. A.

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]

J. A. Jensen, “Field: a program for simulating ultrasound systems,” in 10th Nordicbaltic Conference on Biomedical Imaging (Citeseer, 1996), pp. 351–353.

Jones, D. L.

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.

Jose, J.

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]

Kennedy, B. F.

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]

Kim, G.-D.

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]

Kim, Y.

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]

Koh, L. M.

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]

Kowalczyk, A.

Krishnamurthy, M.

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).

Latifoglu, F.

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]

Leavline, E. J.

Lee, J.-S.

J.-S. Lee, “Speckle analysis and smoothing of synthetic aperture radar images,” Comput. Graph. Image Process. 17, 24–32 (1981).
[CrossRef]

Leitgeb, R. A.

Liu, C.

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]

Liu, J.

Lopez-Molina, C.

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]

Lv, X.-G.

Lyons, M. J.

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]

Ma, J.

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]

Maduro, C.

Malik, J.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).
[CrossRef]

Morel, J.-M.

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]

Muhammed, R.

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]

Nair, M. S.

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]

Natarajan, P.

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]

Noonan, J. P.

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]

O’Brien, W. D.

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.

Perona, P.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).
[CrossRef]

Petrou, M.

C. Tsiotsios and M. Petrou, “On the choice of the parameters for anisotropic diffusion in image processing,” Pattern Recogn. 46, 1369–1381 (2013).
[CrossRef]

Pircher, M.

Prahladan, A.

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]

Qian, J.

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]

Ramamoorthy, K.

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).

Sampson, D. D.

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]

Sattmann, H.

Saunders, C. M.

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]

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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).
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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).
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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]

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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]

Sundararajan, P.

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).

Sutha, S.

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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]

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Szkulmowski, M.

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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).
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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).
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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).
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C. Tsiotsios and M. Petrou, “On the choice of the parameters for anisotropic diffusion in image processing,” Pattern Recogn. 46, 1369–1381 (2013).
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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).
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Y. Gu, Z. Cui, C. Xiu, and L. Wang, “Ultrasound echocardiography despeckling with non-local means time series filter,” Neurocomputing 124, 120–130 (2014).
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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).
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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).
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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).
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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).
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Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).

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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).
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Y. Gu, Z. Cui, C. Xiu, and L. Wang, “Ultrasound echocardiography despeckling with non-local means time series filter,” Neurocomputing 124, 120–130 (2014).
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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).
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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).
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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).
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J. Yu, J. Tan, and Y. Wang, “Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method,” Pattern Recogn. 43, 3083–3092 (2010).
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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).
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Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11, 1260–1270 (2002).
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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).
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Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).

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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]

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Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).

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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]

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).
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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]

IEEE Trans. Image Process. (4)

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11, 1260–1270 (2002).
[CrossRef]

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]

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]

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]

IEEE Trans. Med. Imag. (1)

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]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

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).
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IEEE Trans. Ultrason. Ferroelectr. Freq. Control (1)

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]

Int. J. Comp. Sci. Mobile Comput. (1)

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).

J. Biomed. Opt. (1)

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]

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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).
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Neurocomputing (1)

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]

Opt. Express (3)

Pattern Recogn. (4)

J. Yu, J. Tan, and Y. Wang, “Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method,” Pattern Recogn. 43, 3083–3092 (2010).
[CrossRef]

C. Tsiotsios and M. Petrou, “On the choice of the parameters for anisotropic diffusion in image processing,” Pattern Recogn. 46, 1369–1381 (2013).
[CrossRef]

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]

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]

SIAM J. Numer. Anal. (1)

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]

Ultrasound Med. Biol. (3)

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]

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]

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]

Other (3)

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.

J. A. Jensen, “Field: a program for simulating ultrasound systems,” in 10th Nordicbaltic Conference on Biomedical Imaging (Citeseer, 1996), pp. 351–353.

Y. Zhan, M. Ding, L. Wu, and X. Zhang, “Nonlocal means method using weight refining for despeckling of ultrasound images,” Signal Process. (in press).

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Figures (11)

Fig. 1.
Fig. 1.

Gabor directional templates with 3×3 windows at 16 directions. Pixel intensities in each template have been normalized to a range between 0 and 1 for better visualization.

Fig. 2.
Fig. 2.

Gabor transform of a synthetic US image by using Gabor kernels along 16 directions. (a) A synthetic US image corrupted by speckle noise, (b) images generated by convoluting (a) with 16 templates (3×3) shown in Fig. 1, and (c) detected edges.

Fig. 3.
Fig. 3.

Denoising results of two images simulated by using the multiplicative model with speckle variances of (a) 0.05 and (b) 0.1. Images were filtered by eight methods.

Fig. 4.
Fig. 4.

Denoising results of an image simulated by using Field II. The image was filtered by eight methods.

Fig. 5.
Fig. 5.

Denoising results of an image simulated by using the convolution model. The image was filtered by eight methods.

Fig. 6.
Fig. 6.

Edge detection results using the Gabor-based edge detector with different window sizes of Gabor templates. (a) A noise-free image. (b)–(g) The edge-strength images obtained by using window sizes of (b) 2×2, (c) 3×3, (d) 4×4, (e) 5×5, (f) 6×6, and (g) 7×7. The regions superimposed with rectangles on the top row are enlarged on the bottom row.

Fig. 7.
Fig. 7.

Comparison of edge detectors. (a) Top: a noise-free image; bottom: a simulated US image. (b) The edge-strength images obtained by the Gabor-based edge detector. (c) The edge-strength images obtained by the instantaneous coefficient of variation.

Fig. 8.
Fig. 8.

Homogeneous FFSRs were automatically localized, and the edge magnitude thresholds (k) that calculated on the regions changed through iterations. The top row shows US images superimposed with yellow squares indicating the FFSRs. The bottom row shows plots of the edge magnitude thresholds versus iterations. The synthetic US images were simulated by using (a) the multiplicative model, (b) the Field II simulation, and (c) the convolution model. The image in (d) was a real contrast-enhanced US image of a carotid artery with atherosclerosis.

Fig. 9.
Fig. 9.

Quantitative comparison of denoising performance for the synthetic US images corrupted by different variance of speckle noise with the multiplicative model. The GAD method was compared with seven existing methods. Three quantitative indices were used including the peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM), and Pratt’s figure of merit (FOM).

Fig. 10.
Fig. 10.

Denoising results for a clinical contrast-enhanced US image of a carotid artery with atherosclerosis. The image was filtered by eight methods. The yellow line on the initial noisy image is a sampling line whose pixel intensities are depicted on the plots right to the noisy or denoised images. The rectangles with yellow solid lines and green dashed lines denote the FFSR and the reference homogeneous region, respectively.

Fig. 11.
Fig. 11.

Denoising results for a clinical US image of a breast tumor. The image was filtered by eight methods. The yellow line on the initial noisy image is a sampling line whose pixel intensities are depicted on the plots right to the noisy or denoised images. The rectangles with yellow solid lines and green dashed lines denote the FFSR and the reference homogeneous region, respectively.

Tables (2)

Tables Icon

Table 1. Denoising Performance for Synthetic US Images with Three Simulation Modelsa

Tables Icon

Table 2. Denoising Performance for Clinical US Imagesa

Equations (21)

Equations on this page are rendered with MathJax. Learn more.

h(x,y;v,θ)=(|kv,θ|2/σ2)exp[|kv,θ|2|z|2/(2σ2)]·[exp(jkv,θz)exp(σ2/2)],
kv,θ=(kmax/fv)·(cosθ,sinθ),
g(x,y;θd)=(|kd|2/σ2)exp[|kd|2|z|2/(2σ2)]sin(kdz),
kd=kmax(cosθd,sinθd).
Gd(x,y)=I(x,y)*g(x,y;θd),
{Gsd(x,y)=1D1d=0D1[Gd(x,y)G¯(x,y)]2G¯(x,y)=d=0D1Gd(x,y)/D.
Gsd(x,y)=1D1d=0D1[Gd(x,y)]2.
{It=div[c(|I|)·I]I(t=0)=I0,
{It=div[c(Gsd)·I]I(t=0)=I0,
c(Gsd)=1/[1+(Gsd/k)2].
s*=argmins[meanΩs(Gsd,0)].
k=medianΩ[|GsdmedianΩ(Gsd)|]·α/0.6745,
MAEΩ=1P2(i,j)Ω|In(i,j)In1(i,j)|,
{t=nΔt,n=0,1,2,x=ih,i=0,1,2,,M1y=jh,j=0,1,2,,N1,
Ii,jn+1=Ii,jn+Δt[ci+1,jn(Ii+1,jnIi,jn)+ci1,jn(Ii1,jnIi,jn)+ci,j+1n(Ii,j+1nIi,jn)+ci,j1n(Ii,j1nIi,jn)],
ci,jn=11+[Gsd(i,j,nΔt)/k(nΔt)]2.
{I1,jn=I0,jn,IN+1,jn=IN,jn,j=0,1,2,,NIi,1n=Ii,0n,Ii,M+1n=Ii,Mn,i=0,1,2,,M.
Is(x,y)=[1+ns(x,y)]I(x,y).
V(x,y)=T(x,y)*{sin(k0x)exp[x2/(2σx2)]exp[y2/(2σy2)]},
Is(x,y)=log10|V(x,y)+jV^(x,y)|,
CNR=|μ1μ2|/σ12+σ22,

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