Skip to main content
Springer Nature Link
Log in

Complexity reduction method for ultrasound imaging enhancement in tetrolet transform domain

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Medical ultrasound images are often very obscure due to the capture devices used. As medical ultrasound images can provide information regarding potential pathological changes and abnormalities, images must be preserved and enhanced to help doctors to monitor the course of diseases. This work proposes using the energy concentration characteristics of tetrolet transform (TT) to preserve texture information and integrate the technique of adaptive histogram equalization (AHE) in order to automatically adjust the ultrasound image contrast, thereby resolving the aforementioned problems. However, the implementation of the TT in software structure brings issues such as computational complexity and large frame buffer use. In view of these problems, this work proposes a low-complexity matching pattern (LMP) method to reduce the space complexity. The LMP is combined with a look-up table approach that solves the issue of the excessive calculations generated in AHE during linear interpolation. The experimental results indicate that the modified algorithm is applicable in ultrasound imaging systems as it is able to improve calculation times.

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

Similar content being viewed by others

References

  1. Gupta S, Kaur Y (2014) Review of different local and global contrast enhancement techniques for a digital image. Int J Comput Appl 100(18):18–23

    Google Scholar 

  2. Zhu H, Chan FHY, Lam FK (1999) Image contrast enhancement by constrained local histogram equalization. Comput Vis Image Underst 73(2):281–290

    Article  Google Scholar 

  3. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Gr Image Process 39(3):355–368

    Article  Google Scholar 

  4. Wang Z, Tao J (2006) A fast implementation of adaptive histogram equalization. In: IEEE International Conference on Signal Processing, vol 2

  5. Sanny, A, Yang YHE, Prasanna VK (2014) Energy-efficient histogram equalization on FPGA. In: IEEE High Performance Extreme Computing Conference, pp 293–299

  6. Kim J-Y, Kim L-S, Hwang S-H (2001) An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans Circuits Syst Video Technol 11(4):475–484

    Article  Google Scholar 

  7. Krommweh J (2010) Tetrolet transform: a new adaptive Haar wavelet algorithm for sparse image representation. J Vis Commun Image Represent 21(4):364–374

    Article  Google Scholar 

  8. Gao J, Dong Y, Shen Y, Zhang C (2015) Research of image fusion algorithm based on improved tetrolet transform. Comput Sci 42(5):320–323

    Google Scholar 

  9. Kishore KLN, Prathibha G (2015) Multi-focus medical image fusion using tetrolet transform based on global thresholding approach. Int J Sci Res 4(1):518–522

    Google Scholar 

  10. Wang J, Lian S, Shi Y (2017) Hybrid multiplicative multi-watermarking in DWT domain. Multidimens Syst Signal Process 28(2):617–636

    Article  Google Scholar 

  11. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2017) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl 76(22):23721–23737

    Article  Google Scholar 

  12. Hsia C-H, Chiang J-S, Guo J-M (2013) Memory-efficient hardware architecture of 2-D dual-mode lifting-based discrete wavelet transform. IEEE Trans Circuits Syst Video Technol 23(4):671–683

    Article  Google Scholar 

  13. Krommweh J, Ma J (2010) Tetrolet shrinkage with anisotropic total variation minimization for image approximation. Signal Process 90(8):2529–2539

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for the many helpful suggestions. This work was supported by the Ministry of Science and Technology of Taiwan under Grant Number MOST 104-2221-E-034-013-MY2.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan

    Chih-Hsien Hsia

  2. Department of Electrical and Computer Engineering, Tamkang University, Tamsui, Taiwan

    Jia-Hao Yang & Jen-Shiun Chiang

Authors
  1. Chih-Hsien Hsia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia-Hao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jen-Shiun Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jen-Shiun Chiang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsia, CH., Yang, JH. & Chiang, JS. Complexity reduction method for ultrasound imaging enhancement in tetrolet transform domain. J Supercomput 76, 1438–1449 (2020). https://doi.org/10.1007/s11227-018-2240-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2240-x

Keywords

Search

Navigation