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Medical Image Compression Based on Wavelets with Particle Swarm Optimization

Monagi H. Alkinani1,*, E. A. Zanaty2, Sherif M. Ibrahim3

1 Department of Computer Science and Artificial Intelligence, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabi
2 Department of Computer Science, Faculty of Computers and Information, Sohag University, Sohag, Egypt
3 Department of Computer Science and Mathematics, Faculty of Science, South Valley University, Qena, Egypt

* Corresponding Author: Monagi H. Alkinani. Email: email

Computers, Materials & Continua 2021, 67(2), 1577-1593. https://doi.org/10.32604/cmc.2021.014803

Abstract

This paper presents a novel method utilizing wavelets with particle swarm optimization (PSO) for medical image compression. Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding. It transfers images into subband details and approximations using a modified Haar wavelet (MHW), and then applies a threshold. PSO is applied for selecting a particle assigned to the threshold values for the subbands. Nine positions assigned to particles values are used to represent population. Every particle updates its position depending on the global best position (gbest) (for all details subband) and local best position (pbest) (for a subband). The fitness value is developed to terminate PSO when the difference between two local best (pbest) successors is smaller than a prescribe value. The experiments are applied on five different medical image types, i.e., MRI, CT, and X-ray. Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio (PSNR) and compression ratio (CR) points of views.

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Cite This Article

M. H. Alkinani, E. A. Zanaty and S. M. Ibrahim, "Medical image compression based on wavelets with particle swarm optimization," Computers, Materials & Continua, vol. 67, no.2, pp. 1577–1593, 2021.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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