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Optimized Medical Image Compression for Telemedicine Applications

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Artificial Intelligence and Data Mining in Healthcare

Abstract

Efficient compression of a huge number of medical images becomes necessary for storing and transmitting in telemedicine applications. In this paper, an algorithm is proposed for highly efficient compression of 2D medical images. The proposed algorithm used Legendre moments to extract the features from images and the whale optimization algorithm (WOA) to select which of these moments are the optimum to be used in the reconstruction process and in turn will produce the optimum reconstruction quality. The proposed algorithm aims to achieve higher compression ratios while maintaining the quality of the images. Medical images from different imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-ray images are used in testing the proposed algorithm. The mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized correlation coefficient (NCC) are quantitative measures used to evaluate the performance of the proposed algorithm and well-known existing medical image compression methods. The results showed that the quality of the reconstructed images using the proposed algorithm is much better than those of the conventional 2D compression algorithms in terms of MSE, PSNR, SSIM, and NCC.

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Authors and Affiliations

  1. Zagazig University, Zagazig, Egypt

    Khalid M. Hosny, Asmaa M. Khalid & Ehab R. Mohamed

Authors
  1. Khalid M. Hosny
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  2. Asmaa M. Khalid
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  3. Ehab R. Mohamed
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Editor information

Editors and Affiliations

  1. LASPI, G037, IUT de Roanne, Roanne, France

    Malek Masmoudi

  2. Department of Business, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Bassem Jarboui

  3. Laboratoire LiSSi (EA 3956), Université Paris-Est Créteil Val-de-Marne, Créteil, France

    Patrick Siarry

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Hosny, K.M., Khalid, A.M., Mohamed, E.R. (2021). Optimized Medical Image Compression for Telemedicine Applications. In: Masmoudi, M., Jarboui, B., Siarry, P. (eds) Artificial Intelligence and Data Mining in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-030-45240-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-45240-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45239-1

  • Online ISBN: 978-3-030-45240-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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