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    Abstract
2017 (Vol. 14, Issue: 9)
Research Article

Volumetric Medical Images Lossy Compression using Stationary Wavelet Transform and Linde-Buzo-Gray Vector Quantization

1Hend A. Elsayed, 2Omar G. Abood and 2Shawkat K. Guirguis
1Department of Communication and Computer Engineering Faculty of Engineering, Delta University for Science and Technology, Mansoura
2Department of Information Technology, Institute of Graduate Studies and Researches, Alexandria University, Egypt
 

DOI: 10.19026/rjaset.14.5134
Submitted Accepted Published
January 1, 2017 August 14, 2017 September15, 2017

  How to Cite this Article:

1Hend A. Elsayed, 2Omar G. Abood and 2Shawkat K. Guirguis, 2017. Volumetric Medical Images Lossy Compression using Stationary Wavelet Transform and Linde-Buzo-Gray Vector Quantization.  Research Journal of Applied Sciences, Engineering and Technology, 14(9): 352-360.

DOI: 10.19026/rjaset.14.5134

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.5134

Abstract:


The aim of the study is to reduce the size required for storage along with decreasing the bitrate and the bandwidth for the process of sending and receiving the image. It also aims to decrease the time required for the process as much as possible. This study proposes a novel system for efficient lossy volumetric medical image compression using Stationary Wavelet Transform and Linde-Buzo-Gray for Vector Quantization. The system makes use of a combination of Linde-Buzo-Gray vector quantization technique for lossy compression along with Arithmetic coding and Huffman coding for lossless compression. The system proposed uses Stationary Wavelet Transform and then compares the results obtained to Discrete Wavelet Transform, Lifting Wavelet Transform and Discrete Cosine Transform at three decomposition levels. The system also compares the results obtained using transforms with only Arithmetic Coding and Huffman Coding for Lossless Compression.The results show that the system proposed outperforms the others.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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