Optimal Compression of Medical Images

title = "Optimal Compression of Medical Images",

journal = "International Journal of Advanced Computer Science and Applications(IJACSA)",

publisher = "The Science and Information (SAI) Organization",

year = "2019",

number = "4",

volume = "10",

keywords = "genetic algorithms, genetic programming, medical images, wavelet transform, JPEG2000, compression, quantization",

URL = "

http://thesai.org/Downloads/Volume10No4/Paper_15-Optimal_Compression_of_Medical_Images.pdf",

DOI = "

doi:10.14569/IJACSA.2019.0100415",

abstract = "In todays healthcare system, medical images are playing a vital role in the diagnosis. The challenges arise to the hospital management systems (HMS) are to store and communicate the large volume of medical images generated by various imaging modalities. Efficient compression of medical images is required to reduce the bit rate to increase the storage capacity and speed-up the transmission without affecting its quality. Over the past few decades, several compression standards have been proposed. In this paper, an intelligent JPEG2000 compression scheme is presented to compress the medical images efficiently. Unlike the traditional compression techniques, genetic programming (GP)-based quantisation matrices are used to quantise the wavelet coefficients of the input image. Experimental results validate the usefulness of the proposed intelligent compression scheme.",

bibsource = "OAI-PMH server at thesai.org",

language = "eng",

oai = "oai:thesai.org:10.14569/IJACSA.2019.0100415",