Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

Analysis of Wavelet-based Transform Compression Techniques on Medical Image

A. O. Ajao, T. S. Ibiyemi Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
Download full text
  1. A O Ajao and T S Ibiyemi. Article: Analysis of Wavelet-based Transform Compression Techniques on Medical Image. International Journal of Applied Information Systems 9(4):64-68, July 2015. BibTeX

    	author = "A. O. Ajao and T. S. Ibiyemi",
    	title = "Article: Analysis of Wavelet-based Transform Compression Techniques on Medical Image",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 4,
    	pages = "64-68",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


This paper is aimed at analyzing the performance of three different state-of-the-art image compression schemes namely Embedded Zerotree Wavelet (EZW), Spatial-orientation Trees Wavelet (STW) and Set Partitioning in Hierarchical Trees (SPHIT). The paper analyses the compression schemes using X-ray image data such that the quality of the reconstructed image would be closely related to its original image after 20 iterations of the compression steps. We compared the compression ratio and bit per pixel against the peak signal to noise ratio respectively for X-ray images represented in various image sizes of 256x256 and 512x512. Also, we discussed the characteristics on quality performance used. Some tests conducted for comparing them and the compression quality are addressed in this paper and the quality of compression is determined from the metrics of compression ratio (CR), bit per pixel (BPP), mean square error (MSE) and peak signal to noise ratio (PSNR).


  1. Salomon, D. 2007. Data Compression: The Complete Reference. 4th ed. London: Springer-Verlag.
  2. Dougherty, G. 2009. Digital Image Processing for Medical Applications. New York, USA: Cambridge University Press.
  3. Solomon, C. and Breckon, T. 2011. Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Oxford, UK: Wiley-Blackwell.
  4. Lin, C. , Lee, M. , Pan, S. and Lu, C. 2006. Wavelet compression algorithm applied to abdominal ultrasound images. Australian Institute of Radiography; 53: 11-17.
  5. Neri, E. , Caramella, D. and Bartolozzi, C. 2008. Image Processing in Radiology: Current Applications. eds. New York: Springer Berlin Heidelberg.
  6. Gonzalez, R. C. , Woods, R. E. and Eddins, S. L. 2004. Digital Image Processing Using Matlab. Upper Saddle River, NJ: Pearson Prentice Hall.
  7. Hwang, W. J. , Chine, C. F. and Li, K. J. 2003. Scalable medical data compression and transmission using wavelet transform for telemedicine applications, IEEE Trans. on Info Tech in Biomed; 7: 54-63.
  8. Radha, V. , and Pushpalakshmi. 2010. Performance analysis of lossy compression algorithm for medical images. J Global Res Comp Sci; 1: 4.
  9. Sudha, V. K. and Sudhakar, R. 2011. Two dimensional medical image compression techniques-A survey. J ICGST-GVIP.
  10. Meyer-Base, A. 2004. Pattern Recognition for Medical Imaging. San Diego, California, USA: Elsevier Academic Press.
  11. Acharya, T. and Ray A. K. 2005. Image Processing: Principles and Applications. Hoboken, New Jersey: Wiley-Interscience.
  12. Burrus, C. , Gopinath, R. and Guo, H. 1998. Introduction to Wavelets and Wavelet Transforms – a Primer. Houston, Texas: Prentice Hall International.
  13. Misiti, M. , Misiti, Y. , Oppenheim, G. and Poggi, J-M. 1996-1997. Wavelet Toolbox for use with MATLAB®. User's Guide Version 2. Natick: The Mathsworks.
  14. Soman, K. P. , Ramachandran, K. I. and Resari, N. G. 2004. Insight into Wavelets: From Theory to Practice. India Pvt: Prentice-Hall.
  15. Mallat, S. 1998. Wavelet Tour of Signal Processing. USA: Academic Press.
  16. Sayood, K. 2005. Introduction to Data Compression. 3rd ed. San Francisco, CA: Morgan Kaufmann.
  17. Shapiro, J. M. 1993. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Proc; 41: 3445-3462.
  18. Amir, S. and William, A. P. 1996. A new fast/efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ & Syst for Video Tech; 6: 243-250.
  19. Misiti, M. , Misiti, Y. , Oppenheim, G. and Poggi, J-M. 2007. Wavelets and their Applications. Edited. ISTE USA, Newport Beach.
  20. Thyagarajan, K. S. 2011. Still Image and Video Compression with Matlab. New Jersy: John Wiley & Sons publication.
  21. Chang, S. G, Yu, B. and Vetterli, M. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans on Image Proc; 9: 1532-1546.
  22. Mousa, A. and Odeh, N. 2012. Optimizing the wavelet parameters to improve image compression. Int J Adv in Eng &Tech; 4: 46-52.
  23. Saffor, A. and Ramli, A. R. 2001. A comparative study of image compression between JPEG and wavelet. Malay J Comp Sci; 14: 39-45.
  24. Kaushik, A and Gupta, M. 2012. Analysis of image compression algorithms. Int J Eng Res & Appl; 2: 773-779.


Compression, X-ray Images, Discreet Wavelet Transform, Bit-rate.