Abstract

Phase-only filters (POF) are studied for speckle reduction and edge detection in ultrasonic images. A methodology is developed for selecting the filters and compounding the filtered outputs. Studies on four speckled images show that the parametric images of compounded phases highlighted the boundaries. Estimating the heterogeneity index defined as the ratio of the arithmetic to the geometric mean of the magnitudes, the boundaries were also highlighted, providing a second means of detecting boundaries. This technique based on diversity created through a bank of POF reduces the speckle as well as highlights the boundaries of targetlike regions.

© 2009 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. C. B. Burckhardt, “Speckle in ultrasound B-mode scans,” IEEE Trans. Sonics Ultrason. 25, 1-6 (1978).
    [CrossRef]
  2. T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).
  3. D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
    [CrossRef]
  4. R. G. Dantas and E. T. Costa, “Ultrasound speckle reduction using modified Gabor filters,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 530-538 (2007).
    [CrossRef]
  5. P. M. Shankar, “Contrast enhancement and phase sensitive boundary detection in ultrasonic speckle using Bessel spatial filters,” IET Image Process. 3, 41-51 (2009).
    [CrossRef]
  6. J. M. H. du Buff, “Gabor phase in texture estimation,” Signal Process. 21, 221-240 (1990).
    [CrossRef]
  7. P. Moreno, A. Bernardino, and J. Santos-Victor, “Gabor parameter selection for local feature detection,” presented at the Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 7-9 June 2005.
  8. C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
    [CrossRef] [PubMed]
  9. J. L. Horner and P. D. Gianino, “Phase-only matched filtering,” Appl. Opt. 23, 812-816 (1984).
    [CrossRef] [PubMed]
  10. J. L. Horner and J. R. Leger, “Pattern recognition with binary phase-only filters,” Appl. Opt. 24, 609-611 (1985).
    [CrossRef] [PubMed]
  11. H. Goto, T. Konishi, and K. Itoh, “Simultaneous amplitude and phase modulation by a discrete phase-only filter,” Opt. Lett. 34, 641-643 (2009).
    [CrossRef] [PubMed]
  12. J. P. Kirk and A. L. Jones, “Phase-only complex-valued spatial filter,” J. Opt. Soc. Am. 61, 1023-1028 (1971).
    [CrossRef]
  13. J. I. Trisnadi, “Hadamard speckle contrast reduction,” Opt. Lett. 29, 11-13 (2004).
    [CrossRef] [PubMed]
  14. J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
    [CrossRef]
  15. B. V. K. V. Kumar and Z. Bahri, “Phase-only filters with improved signal to noise ratio,” Appl. Opt. 28, 250-257 (1989).
    [CrossRef] [PubMed]
  16. B. Bhaduri, N. K. Mohan, M. P. Kothiyal, and R. S. Sirohi, “Use of spatial phase shifting technique in digital speckle pattern interferometry (DSPI) and digital shearography (DS),” Opt. Express 14, 11598-11607 (2006).
    [CrossRef] [PubMed]
  17. D. G. Brennan, “Linear diversity combining techniques,” Proc. IRE 47, 1075-1102 (1959).
    [CrossRef]
  18. P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
    [CrossRef]
  19. J. R. Sanchez and M. L. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 1327-1339(2009).
    [CrossRef] [PubMed]
  20. A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529-541 (1981).
    [CrossRef]
  21. R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
    [CrossRef]
  22. M. K. Simon and M.-S. Alouni, Digital Communication over Fading Channels: A Unified Approach to Performance Analysis (Wiley, 2000).
    [CrossRef]
  23. P. M. Shankar, “A general statistical model for ultrasonic scattering from tissues,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47, 727-736 (2000).
    [CrossRef]
  24. P. M. Shankar, “A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 50, 339-343 (2003).
    [CrossRef] [PubMed]
  25. P. M. Shankar, “A model for ultrasonic scattering from tissues based on K distribution,” Phys. Med. Biol. 40, 1633-1649(1995).
    [CrossRef] [PubMed]
  26. C. J. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proc. Radar Sonar Navig. 143, 391-399 (1996).
    [CrossRef]
  27. R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
    [CrossRef]
  28. C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
    [CrossRef]

2009

P. M. Shankar, “Contrast enhancement and phase sensitive boundary detection in ultrasonic speckle using Bessel spatial filters,” IET Image Process. 3, 41-51 (2009).
[CrossRef]

H. Goto, T. Konishi, and K. Itoh, “Simultaneous amplitude and phase modulation by a discrete phase-only filter,” Opt. Lett. 34, 641-643 (2009).
[CrossRef] [PubMed]

J. R. Sanchez and M. L. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 1327-1339(2009).
[CrossRef] [PubMed]

2007

R. G. Dantas and E. T. Costa, “Ultrasound speckle reduction using modified Gabor filters,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 530-538 (2007).
[CrossRef]

2006

2004

J. I. Trisnadi, “Hadamard speckle contrast reduction,” Opt. Lett. 29, 11-13 (2004).
[CrossRef] [PubMed]

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

2003

D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
[CrossRef]

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

P. M. Shankar, “A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 50, 339-343 (2003).
[CrossRef] [PubMed]

2001

C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
[CrossRef] [PubMed]

2000

P. M. Shankar, “A general statistical model for ultrasonic scattering from tissues,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47, 727-736 (2000).
[CrossRef]

1996

C. J. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proc. Radar Sonar Navig. 143, 391-399 (1996).
[CrossRef]

C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
[CrossRef]

1995

P. M. Shankar, “A model for ultrasonic scattering from tissues based on K distribution,” Phys. Med. Biol. 40, 1633-1649(1995).
[CrossRef] [PubMed]

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

1990

J. M. H. du Buff, “Gabor phase in texture estimation,” Signal Process. 21, 221-240 (1990).
[CrossRef]

1989

1988

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
[CrossRef]

1985

1984

1981

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529-541 (1981).
[CrossRef]

1978

C. B. Burckhardt, “Speckle in ultrasound B-mode scans,” IEEE Trans. Sonics Ultrason. 25, 1-6 (1978).
[CrossRef]

1971

1959

D. G. Brennan, “Linear diversity combining techniques,” Proc. IRE 47, 1075-1102 (1959).
[CrossRef]

Allan, S. M.

D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
[CrossRef]

Alouni, M.-S.

M. K. Simon and M.-S. Alouni, Digital Communication over Fading Channels: A Unified Approach to Performance Analysis (Wiley, 2000).
[CrossRef]

Bahri, Z.

Bernardino, A.

P. Moreno, A. Bernardino, and J. Santos-Victor, “Gabor parameter selection for local feature detection,” presented at the Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 7-9 June 2005.

Bhaduri, B.

Blacknell, D.

C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
[CrossRef]

Bonet, J. A.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

Bousquet, P.

R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
[CrossRef]

Brennan, D. G.

D. G. Brennan, “Linear diversity combining techniques,” Proc. IRE 47, 1075-1102 (1959).
[CrossRef]

Burckhardt, C. B.

C. B. Burckhardt, “Speckle in ultrasound B-mode scans,” IEEE Trans. Sonics Ultrason. 25, 1-6 (1978).
[CrossRef]

Chen, C. M.

C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
[CrossRef] [PubMed]

Costa, E. T.

R. G. Dantas and E. T. Costa, “Ultrasound speckle reduction using modified Gabor filters,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 530-538 (2007).
[CrossRef]

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

Dantas, R. G.

R. G. Dantas and E. T. Costa, “Ultrasound speckle reduction using modified Gabor filters,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 530-538 (2007).
[CrossRef]

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

Dennis, M. A.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

du Buff, J. M. H.

J. M. H. du Buff, “Gabor phase in texture estimation,” Signal Process. 21, 221-240 (1990).
[CrossRef]

Gianino, P. D.

Goto, H.

Han, K. C.

C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
[CrossRef] [PubMed]

Horner, J. L.

Howlett, D. C.

D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
[CrossRef]

Itoh, K.

Jones, A. L.

Jones, J. P.

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

Karpur, P.

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

Kirk, J. P.

Konishi, T.

Kothiyal, M. P.

Kumar, B. V. K. V.

Leeman, S.

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

Leger, J. R.

Lim, J. S.

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529-541 (1981).
[CrossRef]

Lombardo, P.

C. J. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proc. Radar Sonar Navig. 143, 391-399 (1996).
[CrossRef]

Lopes, A.

R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
[CrossRef]

Lu, H. H. S.

C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
[CrossRef] [PubMed]

Marchbank, N. D. P.

D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
[CrossRef]

Márquez, I.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

Mohan, N. K.

Moreno, P.

P. Moreno, A. Bernardino, and J. Santos-Victor, “Gabor parameter selection for local feature detection,” presented at the Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 7-9 June 2005.

Muller, R.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

Newhouse, V. L.

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

Oelze, M. L.

J. R. Sanchez and M. L. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 1327-1339(2009).
[CrossRef] [PubMed]

Oliver, C. J.

C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
[CrossRef]

C. J. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proc. Radar Sonar Navig. 143, 391-399 (1996).
[CrossRef]

Oppenheim, A. V.

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529-541 (1981).
[CrossRef]

Parker, S. H.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Rapp, C. L.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Rose, J. L.

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

Sanchez, J. R.

J. R. Sanchez and M. L. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 1327-1339(2009).
[CrossRef] [PubMed]

Santos-Victor, J.

P. Moreno, A. Bernardino, and J. Santos-Victor, “Gabor parameter selection for local feature detection,” presented at the Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 7-9 June 2005.

Shankar, P. M.

P. M. Shankar, “Contrast enhancement and phase sensitive boundary detection in ultrasonic speckle using Bessel spatial filters,” IET Image Process. 3, 41-51 (2009).
[CrossRef]

P. M. Shankar, “A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 50, 339-343 (2003).
[CrossRef] [PubMed]

P. M. Shankar, “A general statistical model for ultrasonic scattering from tissues,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47, 727-736 (2000).
[CrossRef]

P. M. Shankar, “A model for ultrasonic scattering from tissues based on K distribution,” Phys. Med. Biol. 40, 1633-1649(1995).
[CrossRef] [PubMed]

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

Simon, M. K.

M. K. Simon and M.-S. Alouni, Digital Communication over Fading Channels: A Unified Approach to Performance Analysis (Wiley, 2000).
[CrossRef]

Sirohi, R. S.

Sisney, G. A.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Sobotka, M.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

Stavros, T. A.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Thickman, D.

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Touzi, R.

R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
[CrossRef]

Trisnadi, J. I.

Tritschler, A.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

Valadares Oliveira, E. J.

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

White, R. G.

C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
[CrossRef]

Appl. Opt.

Astron. Astrophys.

J. A. Bonet, I. Márquez, R. Muller, M. Sobotka, and A. Tritschler, “Phase diversity restoration of sunspot images I. Relations between penumbral and photospheric features,” Astron. Astrophys. 423, 737-744 (2004).
[CrossRef]

IEE Proc. Radar Sonar Navig.

C. J. Oliver, D. Blacknell, and R. G. White, “Optimum edge detection in SAR,” IEE Proc. Radar Sonar Navig. 143, 31-40(1996).
[CrossRef]

C. J. Oliver and P. Lombardo, “Simultaneous mean and texture edge detection in SAR clutter,” IEE Proc. Radar Sonar Navig. 143, 391-399 (1996).
[CrossRef]

IEEE Trans. Geosci. Remote Sens.

R. Touzi, A. Lopes, and P. Bousquet, “A statistical and geometrical edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 26, 764-773 (1988).
[CrossRef]

IEEE Trans. Sonics Ultrason.

C. B. Burckhardt, “Speckle in ultrasound B-mode scans,” IEEE Trans. Sonics Ultrason. 25, 1-6 (1978).
[CrossRef]

IEEE Trans. Ultrason. Ferroelectr. Freq. Control

R. G. Dantas and E. T. Costa, “Ultrasound speckle reduction using modified Gabor filters,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 530-538 (2007).
[CrossRef]

P. M. Shankar, “A general statistical model for ultrasonic scattering from tissues,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47, 727-736 (2000).
[CrossRef]

P. M. Shankar, “A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 50, 339-343 (2003).
[CrossRef] [PubMed]

J. R. Sanchez and M. L. Oelze, “An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 1327-1339(2009).
[CrossRef] [PubMed]

IET Image Process.

P. M. Shankar, “Contrast enhancement and phase sensitive boundary detection in ultrasonic speckle using Bessel spatial filters,” IET Image Process. 3, 41-51 (2009).
[CrossRef]

J. Diagn. Radiogr. Imag.

D. C. Howlett, N. D. P. Marchbank, and S. M. Allan, “Sonographic assessment of the symptomatic breast--a pictorial review,” J. Diagn. Radiogr. Imag. 5, 3-12 (2003).
[CrossRef]

J. Opt. Soc. Am.

Opt. Express

Opt. Lett.

Phys. Med. Biol.

P. M. Shankar, “A model for ultrasonic scattering from tissues based on K distribution,” Phys. Med. Biol. 40, 1633-1649(1995).
[CrossRef] [PubMed]

Proc. IEEE

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529-541 (1981).
[CrossRef]

Proc. IRE

D. G. Brennan, “Linear diversity combining techniques,” Proc. IRE 47, 1075-1102 (1959).
[CrossRef]

Proc. SPIE

R. G. Dantas, S. Leeman, E. T. Costa, J. P. Jones, and E. J. Valadares Oliveira, “Phase diversity for speckle reduction,” Proc. SPIE 5035, 414-422 (2003).
[CrossRef]

Radiology (Oak Brook, Ill.)

T. A. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” Radiology (Oak Brook, Ill.) 196, 123-134 (1995).

Signal Process.

J. M. H. du Buff, “Gabor phase in texture estimation,” Signal Process. 21, 221-240 (1990).
[CrossRef]

Ultrason.

P. Karpur, P. M. Shankar, J. L. Rose, and V. L. Newhouse, “Split spectrum processing: determination of the available bandwidth for spectral splitting,” Ultrason. 26, 204-209(1988).
[CrossRef]

Ultrasound Med. Biol.

C. M. Chen, H. H. S. Lu, and K. C. Han, “A textural approach based on Gabor functions for texture edge detection in ultrasound images,” Ultrasound Med. Biol. 27, 515-534 (2001).
[CrossRef] [PubMed]

Other

P. Moreno, A. Bernardino, and J. Santos-Victor, “Gabor parameter selection for local feature detection,” presented at the Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 7-9 June 2005.

M. K. Simon and M.-S. Alouni, Digital Communication over Fading Channels: A Unified Approach to Performance Analysis (Wiley, 2000).
[CrossRef]

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (11)

Fig. 1
Fig. 1

Absolute values of the transfer functions of the four spatial POFs are plotted. The vertical and horizontal axes represent the two spatial frequency axes, each ranging from π to π rad , corresponding to half the sampling frequency. The overlap among the responses is minimal, which is reflected in the correlation values (Table 1).

Fig. 2
Fig. 2

Absolute values of the transfer functions for θ = π / 2 for different window sizes. The correlations with the filter of θ = π / 2 , respectively, for these window sizes are 0.2781, 0.1572, 0.1126, and 0.0873.

Fig. 3
Fig. 3

Four images used in the study: (a) synthesized image; (b), (c), and, (d) ultrasonic B-mode images of a tissue-mimicking phantom. All these ultrasonic images have identical background regions with 32   scatterers / mm 3 . The target regions have a den sity of scatterers of (b)  2   scat / mm 3 , (c)  4   scat / mm 3 , and (d)  256   scat / mm 3 . Two ROI are also shown in (a): one in the target region and one outside for the purposes of contrast estimation. While the synthesized image (a) is of 450 × 450 points, the other three images are of size 1591 (vertical) × 422 (horizontal) points. Based on the characteristics of the imaging system, each of these images is estimated to be of size 6.13 cm (vertical) and 8.44 cm (horizontal). The vertical bars with numerical values indicate the intensity levels.

Fig. 4
Fig. 4

Probability density function (pdf) of the data from the two ROI are plotted for the images in Fig. 3. The x axis contains the magnitudes in arbitrary units (A.U.) and the y axis is the pdf. The pdfs were estimated using the MATLAB function ksdensity.

Fig. 5
Fig. 5

Block diagram of processing steps. AM, arithmetic mean; GM, geometric mean; MF, median filter, EF, edge visibility factor; and MRC, maximal ratio combiner.

Fig. 6
Fig. 6

Effect of prefiltering on phase: (a) arithmetic mean α ( x , y ) without prefiltering, (b) geometric mean β ( x , y ) without prefiltering, (c) arithmetic mean α ( x , y ) with prefiltering, and (d) geometric mean β ( x , y ) with prefiltering. No median filtering was performed at this stage. The vertical bars with numerical values indicate the intensity levels. All the phase data were normalized to unity.

Fig. 7
Fig. 7

Absolute values of the transfer functions for the composite filters of Eq. (13) are shown. The prefilter had θ = π . There is some overlap between the transfer functions for θ = π / 6 and θ = π / 6 , resulting in higher correlation for these, as seen in Table 2.

Fig. 8
Fig. 8

Processing results of the image in Fig. 3a: (a) input, (b) output of the MRC, (c) median filtered phase map of the arithmetic mean α ( x , y ) , (d) median filtered phase map of the geometric mean β ( x , y ) , (e) map of the edge visibility factor for the input, and (f) map of the edge visibility factor for the output of the MRC algorithm. Note that (a) and (b) are plotted in a logcompressed format. The rest of the plots are in linear scale. All the phase data were normalized to unity. The boundaries are clearly visible in the phase maps. Boundaries are significantly enhanced for the MRC output, as seen in (f). The vertical bars with numerical values indicate the intensity levels.

Fig. 9
Fig. 9

Processing results of the image in Fig. 3b: (a) input, (b) output of the MRC, (c) median filtered phase map of the arithmetic mean α ( x , y ) , (d) median filtered phase map of the geometric mean β ( x , y ) , (e) map of the edge visibility factor for the input, and (f) map of the edge visibility factor for the output of the MRC algorithm. Note that (a) and (b) are plotted in a logcompressed format. The rest of the plots are in linear scale. All the phase data were normalized to unity. The boundaries are clearly visible in the phase maps. Boundaries are significantly enhanced for the MRC output, as seen in (f). The vertical bars with numerical values indicate the intensity levels.

Fig. 10
Fig. 10

Processing results of the image in Fig. 3c: (a) input, (b) output of the MRC, (c) median filtered phase map of the arithmetic mean α ( x , y ) , (d) median filtered phase map of the geometric mean β ( x , y ) , (e) map of the edge visibility factor for the input, and (f) map of the edge visibility factor for the output of the MRC algorithm. Note that (a) and (b) are plotted in a logcompressed format. The rest of the plots are in linear scale. All the phase data were normalized to unity. The boundaries are clearly visible in the phase maps. Boundaries are significantly enhanced for the MRC output, as seen in (f), while the boundary is not visible in (e). The vertical bars with numerical values indicate the intensity levels.

Fig. 11
Fig. 11

Processing results of the image in Fig. 3d: (a) input, (b) output of the MRC, (c) median filtered phase map of the arithmetic mean α ( x , y ) , (d) median filtered phase map of the geometric mean β ( x , y ) , (e) map of the edge visibility factor for the input, and (f) map of the edge visibility factor for the output of the MRC algorithm. Note that (a) and (b) are plotted in a logcompressed format. The rest of the plots are in linear scale. All the data, including the phase, were normalized to unity. The boundaries are clearly visible in the phase maps. Boundaries are significantly enhanced for the MRC output, as seen in (f), while the boundary is not visible in (e). The vertical bars with numerical values indicate the intensity levels.

Tables (3)

Tables Icon

Table 1 Absolute Values of the Correlation Coefficients Among the Filters of Size 7 × 7

Tables Icon

Table 2 Absolute Values of the Correlation Coefficients Among the Composite Filters

Tables Icon

Table 3 Input Contrast and Output Contrast Estimated as per Eq. (11) and Contrast Enhancement (Ratio of Output to Input) After Filtering for the Four Images a

Equations (14)

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

h c ( x , y ) = u ( x , y ) exp [ j ϕ ( x , y ) ] ,
ϕ ( x , y ) = 2 π [ x cos ( θ ) y sin ( θ ) ] .
h ( x , y ) = exp { j 2 π [ x cos ( θ ) y sin ( θ ) ] } = exp { j 2 π N [ n 1 cos ( θ ) n 2 sin ( θ ) ] } , n < n 1 < n , n < n 2 < n .
w ( x , y ) = g ( x , y ) h ( x , y ) ,
h k ( x , y ) = exp { j 2 π N [ n 1 cos ( θ k ) n 2 sin ( θ k ) ] } , k = 1 , 2 , M ,
w k ( x , y ) = g ( x , y ) h k ( x , y ) , k = 1 , 2 , , M .
w k ( x , y ) = A k ( x , y ) exp [ j ψ k ( x , y ) ] ,
G mrc ( x , y ) = k = 1 M | w k ( x , y ) | 2 .
α ( x , y ) = 1 M k = 1 M | ψ k ( x , y ) | ,
β ( x , y ) = k = 1 M | ψ k ( x , y ) | 1 M .
C = | μ 1 μ 2 | σ 1 2 + σ 2 2 .
h 0 ( x , y ) = exp { j 2 π N [ n 1 cos ( θ ) n 2 sin ( θ ) ] } ,
h 0 k ( t ) = h 0 ( t ) h k ( t ) , k = 1 , 2 , 3 , 4.
E ( x , y ) = log e ( | w ( x , y ) | ) log e ( | w ( x , y ) | ) ,

Metrics