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EZW, SPIHT and WDR Methods for CT Scan and X-ray Images Compression Applications

  • S. Saradha Rani
  • G. Sasibhushana Rao
  • B. Prabhakara Rao
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Scanning rate of medical image tools has been significantly improved owing to the arrival of CT, MRI and PET. For medical imagery, storing in less area and not losing its details are vital. So, an efficient technique is necessary for storing in a cost-effective way. In this paper, wavelet is employed to perform decomposition, and image is compressed using Embedded Zero-Tree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT) and Wavelet Difference Reduction (WDR) algorithms. These algorithms are applied to compress X-ray and CT images, and compared using performance metrics. From results, it is seen that compression ratio is better in WDR for all the wavelets than SPHIT and EZW. High compression ratio, 82.47, is obtained with Haar and WDR combination for CT scan, whereas this is 32.89 for Biorthogonal and WDR combination for X-ray. The main objective of this paper is to find the optimal combination of wavelets and image compression techniques.

Keywords

Wavelet transform SPIHT EZW WDR 

Notes

Acknowledgements

The author would like to thank GIMSR, Visakhapatnam, for providing lower abdomen CT scan and shoulder X-ray images to carry out the research work.

References

  1. 1.
    Kumari L, Pandian R, Aran Glenn J (2016) Analysis of multi scale features of compressed medical images. In: International conference on electrical, electronics, and optimization techniques (ICEEOT)Google Scholar
  2. 2.
    Negahban Farnoosh, Shafieian Mohammad Ali (2013) Various novel wavelet based image compression algorithms using a neural network as a predictor. Basic Appl Sci Res 3(6):280–287Google Scholar
  3. 3.
    Gonzalez RC, Woods RE (2009) Digital image processing. Pearson Education, 2nd ednGoogle Scholar
  4. 4.
    Shapiro JM (1997) Embedded image coding using zero trees of wavelet coefficients, pp 108–121Google Scholar
  5. 5.
    Raja SP, Suraliandi A (2010) Performance evaluation on EZW & WDR image compression techniques. In: International conference on communication control and computing technologiesGoogle Scholar
  6. 6.
    Raja SP (2009) Wavelet based image compression: a comparative study. In: 2009 international conference on advances in computing control and telecommunication technologiesGoogle Scholar
  7. 7.
    Said A, Pearlman WA (1993) Image compression using the spatial-orientation tree. In: IEEE international symposium on circuits and systems, Chicago Google Scholar
  8. 8.
    Said A, Pearlman WA (1996) A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ Syst Video Technol 6(3)CrossRefGoogle Scholar
  9. 9.
    Vaish A, Kumar M (2015) WDR coding based image compression technique using PCA. In: 2015 international conference on signal processing and communicationGoogle Scholar
  10. 10.
    Singh P, Singh P (2012) Implementation of SPIHT and WDR algorithms for natural and artificial images using wavelets. In: Fourth international conference on computational intelligence and communication networksGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. Saradha Rani
    • 1
  • G. Sasibhushana Rao
    • 2
  • B. Prabhakara Rao
    • 3
  1. 1.Department of Electronics and Communication EngineeringGITAMVisakhapatnamIndia
  2. 2.Department of Electronics and Communication EngineeringAU College of Engineering, Andhra UniversityVisakhapatnamIndia
  3. 3.Department of Electronics and Communication EngineeringJNTUKKakinadaIndia

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