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

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.

[1]  Priyanka Singh,et al.  Implementation of SPIHT and WDR Algorithms for Natural and Artificial Images Using Wavelets , 2012, 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

[2]  Farnoosh Negahban,et al.  Various Novel Wavelet - Based Image Compression Algorithms Using a Neural Network as a Predictor , 2013 .

[3]  Manoj Kumar,et al.  WDR coding based image compression technique using PCA , 2015, 2015 International Conference on Signal Processing and Communication (ICSC).

[4]  Barry R. Masters,et al.  Digital Image Processing, Third Edition , 2009 .

[5]  A. Suruliandi,et al.  Performance evaluation on EZW & WDR image compression techniques , 2010, 2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES.

[6]  William A. Pearlman,et al.  Image compression using the spatial-orientation tree , 1993, 1993 IEEE International Symposium on Circuits and Systems.

[8]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[9]  N. Narayanan Prasanth,et al.  Wavelet Based Image Compression: A Comparative Study , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.