An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal...

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Bibliographic Details
Published in:Expert systems Vol. 38; no. 1
Main Authors: Geetha, Karuppaiah, Anitha, Veerasamy, Elhoseny, Mohamed, Kathiresan, Shankar, Shamsolmoali, Pourya, Selim, Mahmoud M.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.01.2021
Subjects:
biomedical applications
Biomedical materials
Compression ratio
Evolutionary algorithms
Experimentation
Image compression
Image quality
Image reconstruction
Lion optimization algorithm
LZMA
Markov chains
medical data
Medical imaging
Optimization
Optimization algorithms
Signal to noise ratio
Vector quantization
ISSN:0266-4720, 1468-0394
Online Access:Get full text
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Summary:Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12508