Wavelet Techniques for Medical Images Performance Analysis and Observations with EZW and Underwater Image Processing

Abstract

The digitized image has an important role in the compression. Compression encodes the image using a certain algorithm with less number of bits and decompression decoded this image in original form using a different algorithm. The clinical environment and hospitals are moving towards digitization, computerization and centralization in the field of medical image processing. Content based compressions (CBC) techniques turns more considerable in the field of medical image processing and multimedia. However, CBC techniques alone are not adequate for all medical image processing applications. Therefore, in this paper, the EZW algorithm has been discussed with Haar wavelet and Bior4.4 technique on skeleton images with different type of image quality parameters. The analysis shows that the compression ratio (CR) of EZW with Bior4.4 on skeleton image is 44.19%, MSE is 527.73 and PSNR is 45.45. While, the CR of EZW with Haar on the same image is 40.31%, MSE is 699 and PSNR is 35.34 db. Further EZW can help precise capacity of amount, exterior area and other morph metric capacity of organic stuff, lacking removing them commencing the sea or in the marine environment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Vo, D. T., Sole, J., Yin, P., Gomila, C., & Nguyen, T. Q. (2010). Selective data pruning-based compression using high order edge-directed interpolation. IEEE Transactions on Image Processing, 19(2), 399–409.

    MathSciNet  Article  Google Scholar 

  2. 2.

    Sikora, T. (1995). Low complexity shape-adaptive DCT for coding of arbitrarily shaped image segments. Signal Processing: Image Communication, 7(4–6), 381–395.

    Google Scholar 

  3. 3.

    Morales, A., Agili, S., & Department of Electrical Engineering. (2003). Implementing the SPIHT algorithm in MATLAB. In Proceedings of the 2003 ASEE/WFEO international colloquium copyright, American society for engineering education.

  4. 4.

    Ramaswamy, V. N., Namuduri, K. R., & Ranganathan, N. (2001). Context-based lossless image coding using EZW framework. IEEE Transactions on Circuits and Systems for Video, 11(4), 554–559.

    Article  Google Scholar 

  5. 5.

    Shapiro, J. M. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12), 3445–3462.

    Article  Google Scholar 

  6. 6.

    Sikora, T. (1997). MPEG digital video coding standards. IEEE Signal Processing Magazine, 14(5), 82–100.

    Article  Google Scholar 

  7. 7.

    Weinberger, M. J., et al. (1996). LOCO-I: A low complexity, context-based, lossless image compression algorithm. IEEE Transactions on Image Processing, 6(10), 140–149.

    Google Scholar 

  8. 8.

    Anh, N. T. N., Yang, W. X., & Cai, J. F. (2009). Seam carwing extension: A compression perspective. In Proceedings ACM conference multimedia (pp. 825–828).

  9. 9.

    Lee, Daniel T. (2005). JPEG 2000: Retrospective and new developments. IEEE Transactions on Image Processing, 93(1), 32–41.

    Google Scholar 

  10. 10.

    Chen, C.-F., & Pang, K. K. (1993). The optimal transform of motion-compensated frame difference images in a hybrid coder. IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing, 40(6), 393–397.

    Article  Google Scholar 

  11. 11.

    Wu, Y.-G. (2002). Medical image compression by sampling DCT coefficients. IEEE Transactions on Information Technology in Biomedicine, 6(1), 86–94.

    Article  Google Scholar 

  12. 12.

    Ahmed, N., Natrajan, T., & Rao, K. R. (1984). Discrete cosine transform. IEEE Transactions on Computers, C-23(1), 90–93.

    MathSciNet  Article  Google Scholar 

  13. 13.

    MPEG-2 Video. (1995). ITU-T Recommendation H.262-ISO/IEC 13818-2.

  14. 14.

    Beck, W. M. (1998). Comparison of the measurements and effects of habitat structure on gastropods in rocky intertidal and mangrove habitats. Marine Ecology Progress Series, 169, 165–178.

    Article  Google Scholar 

  15. 15.

    Done, T. J. (1981). Photogrammetry in coral ecology: A technique for the study of change in coral communities. Proceedings 4th International Coral Reef Symposium, 2, 315–320.

    Google Scholar 

  16. 16.

    Fryer, J. G. (1983). Stereoscopic coral maps from underwater photogrammetry. Cartographic Journal, 20, 23–25.

    Article  Google Scholar 

  17. 17.

    van Rooij, J. M., & Videler, J. J. (1996). A simple field method for stereo-photographic length measurement of free- swimming fish: Merits and constraints. Journal of Experimental Marine Biology and Ecology, 195, 237–249.

    Article  Google Scholar 

  18. 18.

    Warren, J. H., & Underwood, A. J. (1986). Effects of burrowing crabs on the topography of mangrove swamps in New South Wales. Journal of Experimental Marine Biology and Ecology, 102, 223–235.

    Article  Google Scholar 

  19. 19.

    Bythell, J. C., Pan, P., & Lee, J. (2001). Three-dimensional morphometric measurements of reef corals using under-water photogrammetry techniques. Coral Reefs, 20, 193–199.

    Article  Google Scholar 

  20. 20.

    Hou, W. W. (2009). A simple underwater imaging model. Optics Letters, 34(17), 2688–2690.

    Article  Google Scholar 

  21. 21.

    Lee, D. (2005). JPEG 2000: Retrospective and new developments. Proceedings of the IEEE, 93(1), 32–41.

    Article  Google Scholar 

  22. 22.

    Wallace, G. K. (1991). JPEG still picture compression standard. Communications of the ACM, 34, 30–44.

    Article  Google Scholar 

  23. 23.

    Yang, M., & Bourbakis, N. (2005). An overview of lossless digital image compression techniques. In Circuits and systems, 2005 48th Midwest symposium, IEEE (vol. 2, pp. 1099–1102).

  24. 24.

    Yan, X., et al. (2004). The coding technique of image with multiple ROI’s using standard maxshift method. In The 30th annual conference of the IEEE industrial electronics society, Busan, Korea (pp. 2077–2080).

  25. 25.

    Haskell, B. G., Puri, A., & Netravali, A. N. (1998). Digital video: An introduction to MPEG-2. Journal of Electronic Imaging, 7(1), 265–266. https://doi.org/10.1117/1.482669.

    Article  Google Scholar 

  26. 26.

    Mohammed, A. A., & Hussein, J. A. (2011). Efficient hybrid transform scheme for medical image compression. International Journal of Computer Applications, 27(7), 0975-8887.

    Google Scholar 

  27. 27.

    Gormish, M., Lee, D., & Marcellin, M. W. (2000). JPEG 2000: Overview, architecture and applications. In Proceedings of the IEEE international conference of image processing, Vancouver.

  28. 28.

    Cruz, D. S., Grosbois, R., & Ebrahimi, T. (2002). JPEG 2000 performance evaluation and assessment. Signal Processing: Image Communication, 1(17), 113–130.

    Google Scholar 

  29. 29.

    Christopoulos, C., Askelof, J., & Larsson, M. (2000). Efficient methods for encoding ROI in the upcoming JPEG 2000 still image coding standard. IEEE Signal Processing Letters, 7(9), 247–249.

    Article  Google Scholar 

  30. 30.

    Luthra, A., Sullivan, G. J., & Wiegand, T. (2003). Special issue on the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology, 13(7), 148–153.

    Article  Google Scholar 

  31. 31.

    Santa-Cruz, D., & Ebrahimi, T. (2000). An analytical study of JPEG 2000 functionalities. Proceedings of the IEEE International Conference on Image Processing, 2, 49–52.

    Article  Google Scholar 

  32. 32.

    Wikipedia. (2017). Wavelets. Retrieved from February 5, 2017 from https://en.wikipedia.org/wiki/Wavelet.

  33. 33.

    Cattani, C. (2008). Shannon wavelet theory. Mathematical Problems in Engineering, Article ID: 164808. https://doi.org/10.1155/2008/164808.

  34. 34.

    Daubechies, I. (1993). Where do wavelets come from. Proceedings of the IEEE, 84, 510–513. https://doi.org/10.1109/5.488696.

    Article  Google Scholar 

  35. 35.

    Meyer, Y. (1993). Wavelets: Algorithm and application. Society for Industrial and Applied Mathematics, Philadelphia, 13–31, 101–105.

    Google Scholar 

  36. 36.

    Shakhakarmi, N. (2012). Quantitative multiscale analytics with different wavelet in 1D voice signals and 2D images. International Journal of Computer Science Issues, 9, 430.

    Google Scholar 

  37. 37.

    Oduola, W., Okafor, N., Omotere, O., & Qian, L. (2015). Experimental study of hierarchical software defined radio controlled wireless sensor network. In Proceedings of IEEE 36th Sarnoff symposium, Newark (pp. 18–23). https://doi.org/10.1109/SARNOF.2015.7324636.

  38. 38.

    Omotere, O., Oduola, W., Zou, N., Li, X., Qian, L., & Kataria, D. (2016). Distributed spectrum monitoring and surveillance using a cognitive radio based test-bed. In Proceedings of IEEE 37th Sarnoff symposium, Newark (pp. 100–105).

  39. 39.

    Oduola, W., Li, X., Qian, L., & Han, Z. (2014). Power control for device-to-device communications as an underlay to cellular system. In Proceedings of 2014 IEEE international conference on communications, Sydney (pp. 5257–5262). https://doi.org/10.1109/ICC.2014.6884156.

  40. 40.

    Kelsey, A. S., & Akujuobi, C. M. (2016). A discrete wavelet transform approach for enhanced security in image steganography. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 5, 10–20. https://doi.org/10.17781/P001978.

    Article  Google Scholar 

  41. 41.

    Cocito, S., et al. (2003). 3D reconstruction of biological objects using underwater video technique and image processing. Journal of Experimental Marine Biology and Ecology, 297, 57–70.

    Article  Google Scholar 

  42. 42.

    Luchinin, A. G., et al. (2017). Nonstationary optical transfer functions of underwater imaging systems. Applied Optics, 56(27), 7518.

    Article  Google Scholar 

  43. 43.

    Sudhakar, M., et al. (2019). Underwater image enhancement using conventional techniques with quality matrics. International Journal of Innovative Technology and Exploring Engineering, 8(7S), ISSN: 2278-30t5.

  44. 44.

    Deng, C. W., Lin, W. S., & Cai, J. F. (2012). Content-based image compression for arbitrary resolution display devices. In Proceedings IEEE international conference communication IEEE transactions on multimedia.

  45. 45.

    Askelof, J., Carlander, M., & Christopoulos, C. (2002). Region of interest coding in JPEG2000. Signal Processing: Image Communication, 17, 105–111.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Javed Miya.

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

Verify currency and authenticity via CrossMark

Cite this article

Miya, J., Ansari, M.A. Wavelet Techniques for Medical Images Performance Analysis and Observations with EZW and Underwater Image Processing. Wireless Pers Commun 116, 1259–1272 (2021). https://doi.org/10.1007/s11277-020-07238-w

Download citation

Keywords

  • Segmentation
  • Medical image compression
  • PSNR
  • MSE
  • CR
  • EZW
  • Underwater image processing
  • 3D modelling