@article {10.3844/jcssp.2023.363.371,
article_type = {journal},
title = {Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression},
author = {Elsayed, Hend A. and Majeed, Qusay E. and Sherbiny , Mohammed M. El},
volume = {19},
number = {3},
year = {2023},
month = {Feb},
pages = {363-371},
doi = {10.3844/jcssp.2023.363.371},
url = {https://thescipub.com/abstract/jcssp.2023.363.371},
abstract = {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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}