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Modified Scheme of Embedded Zero-Tree Wavelet (EZW) Using Vector Quantization and Run Length Encoding for Compressing Medical Images

Publication Type : Journal

Publisher : American Scientific Publishers

Source : Computational and Theoretical Nanoscience

Url : https://www.ingentaconnect.com/contentone/asp/jctn/2018/00000015/f0020006/art00114

Campus : Coimbatore

School : School of Artificial Intelligence

Center : Center for Computational Engineering and Networking

Year : 2018

Abstract : Medical field has witnessed dramatic advance in medical imaging technologies, eventually calling for effective storage and transmission. This paper presents a novel approach using Embedded Zero-tree Wavelet (EZW) Coding and Vector Quantization (VQ) for compressing medical images. Embedded Zero-tree Wavelet (EZW) method is an efficient technique which exploits the zero-tree used by wavelet coefficients to decrease with the scale. The novelty of our method is the use of modified threshold value in EZW process which has reduced the number of levels thereby reducing the time complexity and without compromising the quality as well. Experimental results of the proposed method achieved high PSNR with low bit rate among existing methods, and hence making it a viable choice for medical image transmission.

Cite this Research Publication : Chitra, P., and M. Rani. "Modified scheme of embedded zero-tree wavelet (EZW) using vector quantization and run length encoding for compressing medical images." Journal of Computational and Theoretical Nanoscience 15, no. 6-7 (2018): 2415-2419.

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