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Survey Analysis for Medical Image Compression Techniques

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Communication and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

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Abstract

This paper presents a survey for medical image compression for both lossy and lossless approaches. This survey discusses twenty-five publications with several applied lossy and lossless compression techniques. All approaches are distributed into seven groups based on the applied technique. Fractals, wavelet, region of interest (ROI) and non-region of interest (Non-ROI), and other approaches represent four lossy compression groups. Adaptive block size, least square, and other approaches represent three lossless medical image compression techniques. Technically, medical image communication requires large space to be represented and sent over the network creating a number of challenges in terms of interaction, processing, storage, and transmission operations. Therefore, significant compression ratio (CR) and peak signal-to-noise ratio (PSNR) are always targeted in image communication. Both CR and PSNR are considered in this survey as the main metrics to investigate and evaluate models performance. As a result of this survey analysis, ROI and Non-ROI has shown the best average CR with 91, and wavelet has shown the best average PSNR with 80.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq

    Baidaa A. Al-Salamee & Dhiah Al-Shammary

Authors
  1. Baidaa A. Al-Salamee
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  2. Dhiah Al-Shammary
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Corresponding author

Correspondence to Dhiah Al-Shammary .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Jaipur, India

    Mukesh Kumar Gupta

  3. Birla Institute of Applied Sciences, Nainital, Uttarakhand, India

    G. S. Tomar

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Wang Lipo

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Al-Salamee, B.A., Al-Shammary, D. (2021). Survey Analysis for Medical Image Compression Techniques. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_21

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  • DOI: https://doi.org/10.1007/978-981-16-1089-9_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1088-2

  • Online ISBN: 978-981-16-1089-9

  • eBook Packages: EngineeringEngineering (R0)

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