Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

Journal of computer sciences

article

Hend A. Elsayed¹  Qusay E. Majeed²  Mohammed M. El Sherbiny² 

[1] Department Electrical Engineering, Faculty of Engineering, Damanhour University;Department of Information Technology, Institute of Graduate Studies and Researches, Alexandria University

关键词: Medical Image Compression;    Non-Decimated Wavelet Transform;    Vector Quantization;    K-Means Clustering;    Linde;    Buzo;    and Gray Algorithm;    Run Length Encoding;    Arithmetic Coding;    Huffman Coding;   

DOI  :  10.3844/jcssp.2023.363.371

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

【 授权许可】

CC BY