Skip to main content
Log in

An FPGA-based architecture of DSC–SRI units specially for motion blind ultrasound systems

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Digital scan conversion (DSC) and speckle reduction imaging (SRI) are basic back-end units of an ultrasound imaging system. In a B-mode ultrasonography (USG) system, DSC is necessary to display a two dimensional image of a tissue structure. Speckle noise, present in an USG system, affects the quality of images observed. Moreover, motion blurring is observed while examining non-stationery organs like the heart. To address these issues, a high speed VLSI design of the USG back-end unit is presented in this paper. The back-end is designed in such a way that it can be integrated with the front-end of the USG system. An FPGA platform is used to prototype these units. Massive usage of the parallel-pipelining technique has helped in achieving high throughput at low power. The DSC architecture operates at 14.66 MHz and is capable of scan converting 132 raw frames per second. Similarly, the SRI unit operates at 221 MHz and is capable of de-speckling images of size 640 × 480 at 698 fps. Therefore, it is possible to investigate objects without motion blur using these units. System level integration issues of the DSC–SRI units are also covered in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Healthcare, G.E., Brochure, G.E.: Medical systems. Ultrasound (2009)

  2. Bommanna Raja, K., Madheswaran M., Thyagarajah K.: Ultrasound kidney image analysis for computerized disorder identification and classification using content descriptive power spectral features. J. Med. Syst. 31(5), 307–317 (2007)

    Article  Google Scholar 

  3. Brekke, S., Tegnander, E., Torp, H., Eik-Nes, S.H.: Dynamic 3D ultrasound imaging of the fetal heart. In: Proceedings IEEE Ultrasonics Symposium (ULTSYM’02) , October 2002, vol. 2, pp. 1593–1596 (2002)

  4. Qayyum, A., Chen, D.M., Breiman, R.S., Westphalen, A.C., Yeh, B.M., Jones, K.D., Lu, Y., Coakley, F.V., Callen, P.W.: Evaluation of diffuse liver steatosis by ultrasound, computed tomography, and magnetic resonance imaging: which modality is best?. Clin. Imaging 33(2), 110–115 (2009)

    Article  Google Scholar 

  5. Bonciu, C., Weber, R., Leger, C.: 4D reconstruction of the left ventricle during a single heart beat from ultrasound imaging. Image Vis. Comput. 19(6), 401–412 (2001)

    Article  Google Scholar 

  6. Uchigasaki, S.: Postmortem ultrasound imaging in forensic pathology. In: Forensic Pathology Reviews, vol. 4, pp. 405–412. Humana Press, New Jersey (2006)

  7. Schmid-Wendtner, M.H., Burgdorf W.: Ultrasound scanning in dermatology. Arch. Dermatol. 141(2), 217–224 (2005)

    Article  Google Scholar 

  8. Lee, M.H., Kim, J.H., Park, S.B.: Analysis of a scan conversion algorithm for a real-time sector scanner. IEEE Trans. Med. Imaging 5(2), 96–105 (1986)

    Article  Google Scholar 

  9. Achim, A., Bezerianos, A., Tsakalides, P.: Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20(8), 772–783 (2001)

    Article  Google Scholar 

  10. Chen, H., Varghese, T., Rahko, P.S., Zagzebski, J.A.: Ultrasound frame rate requirements for cardiac elastography: experimental and in vivo results. Ultrasonics 49(1), 98–111 (2009)

    Article  Google Scholar 

  11. Hadhoud, M.M., Dessouky, M.I., El-Samie, F.E.A.: Adaptive image interpolation based on local activity levels. In: Proceedings 20th National Radio Science Conference, vol. C4, pp. 1–8 (2003)

  12. Fritsch, C., Parrilla, M., Martinez, O., Jiménez, D.: A multirate scan conversion method. Ultrasonics 38(1–8), 179–182 (2000)

    Article  Google Scholar 

  13. Mazumdar, B., Mediratta, A., Bhattacharyya, J., Banerjee, S.: A real time speckle noise cleaning filter for ultrasound images. In: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06), pp. 341–346 (2006)

  14. Berkhoff, A.P., Huisman, H.J., Thijssen, J.M., Jacobs, E.M.G.P., Homan, R.J.F.: Fast scan conversion algorithms for displaying ultrasound sector images. Ultrason. Imaging 16, 87–108 (1994)

    Article  Google Scholar 

  15. Richard, W.D., Arthur, R.M.: Real-time ultrasonic scan conversion via linear interpolation of oversampled vectors. Ultrason. Imaging 16(2), 109–123 (1994)

    Article  Google Scholar 

  16. Chang, J.H., Yen, J.T., Sun, L., Shung, K.K.: Implementation of high frame rate digital scan converter for high frequency ultrasound mechanical sector scanner. In: 2006 IEEE Ultrasonics Symposium, pp. 2226–2229 (2006)

  17. Kassem, A., Sawan, M., Boukadoum, M.: A scan conversion cmos implementation for a portable ultrasonic system. In: IEEE CCECE 2003: Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1461–1464 (2003)

  18. Gribbon, K.T., Bailey, D.G.: A novel approach to real-time bilinear interpolation. In: IEEE International Workshop On Electronic Design, Test and Applications, pp. 126–131 (2004)

  19. Keys, R.: Cubic convolution interpolation for digital imaging processing. IEEE Trans. Acoust. Speech Signal Process. 29(6),1153–1160 (1981)

    Article  MathSciNet  Google Scholar 

  20. Ramponi, G.: Warped distance for space-variant linear image interpolation. IEEE Trans. Image Process. 8(5), 629–639 (1999)

    Article  MathSciNet  Google Scholar 

  21. Ma, L., Ma, J., Shen, Y.: Kringing interpoaltion based ultrasound scan conversion algorithm. In: IEEE International Conference on Information Acquisition, pp. 489–493 (2006)

  22. Bera, D., Agarwal, L., Banerjee, S.: Multirate scan conversion of ultrasound images using warped distance based adaptive bilinear interpolation. In: CBMS 2009: 22nd IEEE International Symposium on Computer-Based Medical Systems, 2009, pp. 1–5 (2009)

  23. Logicore, X.: Digital Clock Manager (DCM) Module, vol. DS485, Xilinx, ver. 1.9 edition (2009)

  24. Volder, J.E.: The CORDIC trigonometric computing technique. IRE Trans. Electron. Comput. EC-8(3), 330–334 (1959)

    Article  Google Scholar 

  25. Das, B., Banerjee, S.: A CORDIC based array architecture for complex discrete wavelet transform. In: Proceedings of the 11th Great Lakes symposium on VLSI, pp. 79–84 (2001)

  26. Abd-Elmoniem, K.Z., Youssef, A.B.M., Kadah, Y.M.: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Trans. Biomed. Eng. 49(9), 997–1014 (2002)

    Article  Google Scholar 

  27. Hao, X., Gao, S., Gao, X.: A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Trans. Med. Imaging 18(9), 787–794 (1997)

    Google Scholar 

  28. Guo H., Odegard J.E., Lang M., Gopinath R.A., Selesnick I.W., Burrus C.S.: “Wavelet based speckle reduction with application to SAR based ATD/R,” ICIP, p. 75–79, 1994

  29. Zong, X., Laine, A.F., Geiser, E.A. Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Trans. Med. Imaging 17(4), 532–540 (1998)

    Article  Google Scholar 

  30. Chen, Y., Yin, R., Flynn, P., Broschat, S.: Aggressive region growing for speckle reduction in ultrasound images. Pattern Recogn. Lett. SU-24(4–5), 677–691 (2003)

    Article  Google Scholar 

  31. Burckhardt, C.B.: Speckle in ultrasound b-mode scans. IEEE Trans. Sonics Ultrason. SU25(1), 1–6 (1978)

    Article  Google Scholar 

  32. Loupas, T., Mcdicken, W.N., Allan, P.L.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Circuits Syst. 36(1), 129–135 (1989)

    Article  Google Scholar 

  33. Koo, J.I., Park, S.B.: Speckle reduction with edge preservation in medical ultrasonic images using a homogeneous region growing mean filter. Ultrason. Imaging 13(3), 211–237 (1991)

    Google Scholar 

  34. Karaman, M., Alper Kutay, M., Bozdagi, G.: An adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Trans. Med. Imaging 14(2), 283–292 (1995)

    Article  Google Scholar 

  35. Bamber, J.C., Daft, C.: Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images. Ultrasonics 24, 41–44 (1986)

    Article  Google Scholar 

  36. Bamber, J.C., Phelps, J.V.: A real-time implementation of coherent speckle suppression in b-scan images. Ultrasonics 29, 218–224 (1991)

    Article  Google Scholar 

  37. Hu, J., Hu, X.: Application of median filter to speckle suppression in intravascular ultrasound images. In: Proceedings 1994 2nd Australian New Zealand Conference on Intelligent Information Systems, pp. 302–306 (1994)

  38. Loupas, T., Allan, P.L., McDicken, W.N.: Clinical evaluation of a digital signal-processing device for real-time speckle suppression in medical ultrasonics. Br. J. Radiol. 62(740):761–764 (1989)

    Article  Google Scholar 

  39. de Fontes, F.P.X., Barroso, G.A., Coupe, P., Hellier, P.: Real time ultrasound image denoising. J. Real-Time Image Process. 6, 15–22 (2011)

    Article  Google Scholar 

  40. Bera, D., Banerjee, S.: Pipelined dsp implementation of nonlinear anisotropic diffusion for speckle reduction of usg images. In: 2010 2nd International Conference on Computer Engineering and Technology (ICCET), April 2010, vol. 2,pp. V249–V253 (2010)

  41. Hazra, A., Bhattacharyya, J., Banerjee S.: Real time noise cleaning of ultrasound images. Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, 2004 (CBMS 2004), pp. 379–384 (2004)

  42. Yue, Y., Croitoru, M.M., Bidani, A., Zwischenberger, J.B., Clark, J.W Jr.: Ultrasonic speckle suppression using robust nonlinear wavelet diffusion for LV volume quantification. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS’04, vol. 1, pp. 1609–1612 (2004)

  43. Nasri, M., Nezamabadi-pour, H.: Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing 72(4–6), 1012–1025 (2009)

    Article  Google Scholar 

  44. Yue, Y., Croitoru, M.M., Bidani, A., Zwischenberger, J.B., Clark, J.W Jr. Ultrasonic speckle suppression using robust nonlinear wavelet diffusion for lv volume quantification. In: Engineering in Medicine and Biology Society, 2004. IEMBS ’04. 26th Annual International Conference of the IEEE, vol. 1, pp. 1609–1612 (2004)

  45. Chang, J.H., Yen, J.T., Kirk Shung K.: High-speed digital scan converter for high-frequency ultrasound sector scanners. Ultrasonics 48, 444–452 (2008)

    Article  Google Scholar 

  46. Wang, H., Deng, J., Li, D., Xie, X., Guo, Q., Zheng, Z.: Software implementation of real-time digital scan converter. In: 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010), October 2010, pp. 574–577 (2010)

  47. Bera, D., Agarwal, L., Banerjee, S.: Simulation of digital scan conversion for ultrasound systems using a digital signal processor. Ultrasound (2011, in press)

  48. Healthcare, G.E.: Product guide (2001)

  49. Healthcare, G.E.: Product data. GE Medical Systems, Ultrasound (2001)

  50. Dantas, R.G., Costa, E.T.: Ultrasound speckle reduction using modified Gabor filters. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54(3), 530–538 (2007)

    Article  Google Scholar 

  51. Toonkum, P., Boonvisut, P., Chinrungrueng, C.: Real-time speckle reduction of ultrasound images based on regularized Savitzky-Golay filters. In: The 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE’08, May 2008, pp. 2311–2314 (2008)

  52. Khare A, Khare M, Jeong Y, Kim H, Jeon M (2010) Despeckling of medical ultrasound images using Daubechies complex wavelet transform. Signal Process 90(2):428–439

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank GE Healthcare India Pvt. Ltd. for supporting this research work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kishor Sarawadekar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Biswas, R., Sarawadekar, K., Varna, S. et al. An FPGA-based architecture of DSC–SRI units specially for motion blind ultrasound systems. J Real-Time Image Proc 10, 573–595 (2015). https://doi.org/10.1007/s11554-012-0289-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-012-0289-y

Keywords

Navigation