iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11277-020-07744-x
Filtering Super-Resolution Scan Conversion of Medical Ultrasound Frames | Wireless Personal Communications Skip to main content
Springer Nature Link
Log in

Filtering Super-Resolution Scan Conversion of Medical Ultrasound Frames

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, we consider a challenging problem of reconstruction of high resolution (HR) B-mode ultrasound (US) image by proposing a novel multi-frame based super-resolution (SR) scan conversion framework. This new framework of SR scan conversion reconstructs an improved HR frame by using the scan data of several low resolution (LR) frames. It also unifies the speckle reduction and HR scan conversion in such a way that it has become a single operation to generate a super-resolved image with lesser loss of information. We evaluated the performance of the proposed model on synthetic images, ultrasound simulated (by Field II software) images and real ultrasound image dataset and the comparison is performed against some of the publicly available state-of-the-art ultrasound image enhancement techniques. Significant improvement in image quality has been achieved due to utilization of non-redundant information present in the scan data of the LR frames. We demonstrate the improvement of the proposed technique through the computation of perceptual and quantitative quality metrics, such as, SSIM, PSNR etc. over the recent competing methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Li, Y., & Zagzebski, J. A. (2000). Computer model for harmonic ultrasound imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 47(5), 1259–1272.

    Article  Google Scholar 

  2. Nandi, D., Mukhopadhyay, S., Ghosh, D., & Chakroborty, B. (2018). A novel framework of speckle reducing scan conversion in ultrasound imaging systems. IETE Technical Review, 35(6), 618–630.

    Article  Google Scholar 

  3. Li, X., Hu, Y., Gao, X., Tao, D., & Ning, B. (2010). A multi-frame image super-resolution method. Signal Processing, 90(2), 405–414.

    Article  MATH  Google Scholar 

  4. Lertrattanapanich, S., & Bose, N. K. (2002). High resolution image formation from low resolution frames using Delaunay triangulation. IEEE Transactions on Image Processing, 11(12), 1427–1441.

    Article  MathSciNet  Google Scholar 

  5. Clement, G. T., Huttunen, J., & Hynynen, K. (2005). Superresolution ultrasound imaging using back-projected reconstruction. The Journal of the Acoustical Society of America, 118(6), 3953–3960.

    Article  Google Scholar 

  6. Christensen-Jeffries, K., Browning, R. J., Tang, M. X., Dunsby, C., & Eckersley, R. J. (2014). In vivo acoustic super-resolution and super-resolved velocity mapping using microbubbles. IEEE Transactions on Medical Imaging, 34(2), 433–440.

    Article  Google Scholar 

  7. Bar-Zion, A., Tremblay-Darveau, C., Solomon, O., Adam, D., & Eldar, Y. C. (2016). Fast vascular ultrasound imaging with enhanced spatial resolution and background rejection. IEEE Transactions on Medical Imaging, 36(1), 169–180.

    Article  Google Scholar 

  8. Taxt, T., & Jirik, R. (2004). Superresolution of ultrasound images using the first and second harmonic signal. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 51(2), 163–175.

    Article  Google Scholar 

  9. Goldberg, B. B., Liu, J. B., & Forsberg, F. (1994). Ultrasound contrast agents: a review. Ultrasound in Medicine & Biology, 20(4), 319–333.

    Article  Google Scholar 

  10. Jang, H. J., Lim, H. K., Lee, W. J., Kim, S. H., Kim, K. A., & Kim, E. Y. (2000). Ultrasonographic evaluation of focal hepatic lesions: comparison of pulse inversion harmonic, tissue harmonic, and conventional imaging techniques. Journal of Ultrasound in Medicine, 19(5), 293–299.

    Article  Google Scholar 

  11. Christensen-Jeffries, K., Harput, S., Brown, J., Wells, P. N., Aljabar, P., Dunsby, C., et al. (2017). Microbubble axial localization errors in ultrasound super-resolution imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 64(11), 1644–1654.

    Article  Google Scholar 

  12. Morin, R., Basarab, A., Ploquin, M., & Kouamé, D. (2012). Post-processing multiple-frame super-resolution in ultrasound imaging. In Medical imaging 2012: Ultrasonic imaging, tomography, and therapy (vol. 8320, p. 83201G). International Society for Optics and Photonics.

  13. Stark, H., & Oskoui, P. (1989). High-resolution image recovery from image-plane arrays, using convex projections. JOSA A, 6(11), 1715–1726.

    Article  Google Scholar 

  14. Irani, M., & Peleg, S. (1991). Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3), 231–239.

    Google Scholar 

  15. Papoulis, A. (1975). A new algorithm in spectral analysis and band-limited extrapolation. IEEE Transactions on Circuits and Systems, 22(9), 735–742.

    Article  MathSciNet  Google Scholar 

  16. Nandi, D., Karmakar, J., Kumar, A., & Mandal, M. K. (2019). Sparse representation based multi-frame image super-resolution reconstruction using adaptive weighted features. IET Image Processing, 13(4), 663–672.

    Article  Google Scholar 

  17. Hong, M. C., Kang, M. G., & Katsaggelos, A. K. (1997). Regularized multichannel restoration approach for globally optimal high-resolution video sequence. In Visual communications and image processing’97 (Vol. 3024, pp. 1306–1316). International Society for Optics and Photonics.

  18. Elad, M., & Feuer, A. (1999). Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Transactions on Image Processing, 8(3), 387–395.

    Article  Google Scholar 

  19. Duhamel, P., & Maitre, H. (1999). Multi-channel high resolution blind image restoration. In 1999 IEEE international conference on acoustics, speech, and signal processing. proceedings. icassp99 (Cat. No. 99CH36258) (vol. 6, pp. 3229–3232). IEEE.

  20. Rajan, D., & Chaudhuri, S. (2002). Generation of super-resolution images from blurred observations using an MRF model. Journal of Mathematical Imaging and Vision, 16(1), 5–15.

    Article  MathSciNet  MATH  Google Scholar 

  21. Tsai, R. Y. (1989). Multiple frame image restoration and registration. Advances in Computer Vision and Image Processing, 1, 1715–1989.

    Google Scholar 

  22. Dai, Y., Wang, B., & Liu, D. (2009). A fast and robust super resolution method for intima reconstruction in medical ultrasound. In 2009 3rd International conference on bioinformatics and biomedical engineering (pp. 1–4). IEEE.

  23. Cardona, H. D. V., López-Lopera, A. F., Orozco, Á. A., Álvarez, M. A., Tamames, J. A. H., & Malpica, N. (2015). Gaussian processes for slice-based super-resolution MR images. In International symposium on visual computing (pp. 692–701). Springer, Cham.

  24. Hiremath, P. S., Akkasaligar, P. T., & Badiger, S. (2013). Speckle noise reduction in medical ultrasound images. Intechopen: In Advancements and breakthroughs in ultrasound imaging.

  25. Prabusankarlal, K. M., Manavalan, R., & Sivaranjani, R. (2018). An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images. Applied Computing and Informatics, 14(1), 48–54.

    Article  Google Scholar 

  26. Lee, J. S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2, 165–168.

    Article  Google Scholar 

  27. Loupas, T., McDicken, W. N., & Allan, P. L. (1989). An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Transactions on Circuits and Systems, 36(1), 129–135.

    Article  Google Scholar 

  28. Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.

    Article  Google Scholar 

  29. Behar, V., Adam, D., & Friedman, Z. (2003). A new method of spatial compounding imaging. Ultrasonics, 41(5), 377–384.

    Article  Google Scholar 

  30. Chang, J. H., Kim, H. H., Lee, J., & Shung, K. K. (2010). Frequency compounded imaging with a high-frequency dual element transducer. Ultrasonics, 50(4–5), 453–457.

    Article  Google Scholar 

  31. Li, P. C., & Chen, M. J. (2002). Strain compounding: a new approach for speckle reduction. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control, 49(1), 39–46.

    Article  Google Scholar 

  32. Ullah, H., Amir, M., Haq, I. U., Khan, S. U., Rahim, M. K. A., & Khan, K. B. (2018). Wavelet based de-noising using logarithmic shrinkage function. Wireless Personal Communications, 98(1), 1473–1488.

    Article  Google Scholar 

  33. Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 2, pp. 60–65). IEEE.

  34. Coupé, P., Hellier, P., Kervrann, C., & Barillot, C. (2009). Nonlocal means-based speckle filtering for ultrasound images. IEEE Transactions on Image Processing, 18(10), 2221–2229.

    Article  MathSciNet  MATH  Google Scholar 

  35. Rudin, L. I., & Osher, S. (1994). Total variation based image restoration with free local constraints. In Proceedings of 1st international conference on image processing (Vol. 1, pp. 31–35). IEEE.

  36. Chinnathambi, V., Sankaralingam, E., Thangaraj, V., & Padma, S. (2018). Despeckling of ultrasound images using directionally decimated wavelet packets with adaptive clustering. IET Image Processing, 13(1), 206–215.

    Article  Google Scholar 

  37. Rawat, N., Singh, M., & Singh, B. (2019). Wavelet and total variation based method using adaptive regularization for speckle noise reduction in ultrasound images. Wireless Personal Communications, 106(3), 1547–1572.

    Article  Google Scholar 

  38. Gungor, M. A., & Karagoz, I. (2015). The homogeneity map method for speckle reduction in diagnostic ultrasound images. Measurement, 68, 100–110.

    Article  Google Scholar 

  39. Wang, S., Huang, T. Z., Zhao, X. L., Mei, J. J., & Huang, J. (2018). Speckle noise removal in ultrasound images by first-and second-order total variation. Numerical Algorithms, 78(2), 513–533.

    Article  MathSciNet  MATH  Google Scholar 

  40. Tom, B. C., Katsaggelos, A. K., & Galatsanos, N. P. (1994). Reconstruction of a high resolution image from registration and restoration of low resolution images. In Proceedings of 1st international conference on image processing (vol. 3, pp. 553–557). IEEE.

  41. Tom, B. C., & Katsaggelos, A. K. (1995). Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images. In Proceedings., international conference on image processing (vol. 2, pp. 539–542). IEEE.

  42. Nandi, D., & Mukhopadhyay, S. (2011). Super-resolution on data acquired in polar format. International Journal of Computational Intelligence and Healthcare Informatics, 4(2), 63–73.

    Google Scholar 

  43. Vandewalle, P., Süsstrunk, S., & Vetterli, M. (2006). A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP Journal on Advances in Signal Processing, 2006(1), 071459.

    Article  Google Scholar 

  44. Alkinani, M. H., & El-Sakka, M. R. (2017). Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction. EURASIP Journal on Image and Video Processing, 2017(1), 1–27.

    Article  Google Scholar 

  45. Smith, J. A. (Ed.). (2010). Abdominal Ultrasound E-Book: How, Why and When. Amsterdam: Elsevier.

    Google Scholar 

  46. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  47. Biswas, R., Sarawadekar, K., Varna, S., & Banerjee, S. (2015). An FPGA-based architecture of DSC-SRI units specially for motion blind ultrasound systems. Journal of Real-Time Image Processing, 10(3), 573–595.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, MG Avenue, Durgapur, India

    Dipannita Ghosh, Amish Kumar, Palash Ghosal & Debashis Nandi

  2. Department of Radiology, Zulekha Hospital, Dubai, United Arab Emirates

    Amritendu Mukherjee

Authors
  1. Dipannita Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Palash Ghosal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amritendu Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Debashis Nandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipannita Ghosh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, D., Kumar, A., Ghosal, P. et al. Filtering Super-Resolution Scan Conversion of Medical Ultrasound Frames. Wireless Pers Commun 116, 883–905 (2021). https://doi.org/10.1007/s11277-020-07744-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07744-x

Keywords

Search

Navigation