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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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):280–287
Gonzalez RC, Woods RE (2009) Digital image processing. Pearson Education, 2nd edn
Shapiro JM (1997) Embedded image coding using zero trees of wavelet coefficients, pp 108–121
Raja SP, Suraliandi A (2010) Performance evaluation on EZW & WDR image compression techniques. In: International conference on communication control and computing technologies
Raja SP (2009) Wavelet based image compression: a comparative study. In: 2009 international conference on advances in computing control and telecommunication technologies
Said A, Pearlman WA (1993) Image compression using the spatial-orientation tree. In: IEEE international symposium on circuits and systems, Chicago
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)
Vaish A, Kumar M (2015) WDR coding based image compression technique using PCA. In: 2015 international conference on signal processing and communication
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 networks
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Saradha Rani, S., Sasibhushana Rao, G., Prabhakara Rao, B. (2019). EZW, SPIHT and WDR Methods for CT Scan and X-ray Images Compression Applications. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_142
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_142
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)