International Journal of Medical Imaging

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Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Received: 20 August 2019    Accepted: 17 September 2019    Published: 27 September 2019
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Abstract

Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.

DOI 10.11648/j.ijmi.20190703.11
Published in International Journal of Medical Imaging (Volume 7, Issue 3, September 2019)
Page(s) 57-65
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wavelets Transform, Region Growing, SPIHT, Daubechies, Biorthogonal

References
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Author Information
  • Faculty of Computers & Information, Sohag University, Sohag, Egypt

  • Mathimatics & Computer Science Department, Faculty of Science, South Valley University, Qena, Egypt

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  • APA Style

    Elnomery Allam Zanaty, Sherif Mostafa Ibrahim. (2019). Medical Image Compression Based on Combining Region Growing and Wavelet Transform. International Journal of Medical Imaging, 7(3), 57-65. https://doi.org/10.11648/j.ijmi.20190703.11

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    ACS Style

    Elnomery Allam Zanaty; Sherif Mostafa Ibrahim. Medical Image Compression Based on Combining Region Growing and Wavelet Transform. Int. J. Med. Imaging 2019, 7(3), 57-65. doi: 10.11648/j.ijmi.20190703.11

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    AMA Style

    Elnomery Allam Zanaty, Sherif Mostafa Ibrahim. Medical Image Compression Based on Combining Region Growing and Wavelet Transform. Int J Med Imaging. 2019;7(3):57-65. doi: 10.11648/j.ijmi.20190703.11

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  • @article{10.11648/j.ijmi.20190703.11,
      author = {Elnomery Allam Zanaty and Sherif Mostafa Ibrahim},
      title = {Medical Image Compression Based on Combining Region Growing and Wavelet Transform},
      journal = {International Journal of Medical Imaging},
      volume = {7},
      number = {3},
      pages = {57-65},
      doi = {10.11648/j.ijmi.20190703.11},
      url = {https://doi.org/10.11648/j.ijmi.20190703.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijmi.20190703.11},
      abstract = {Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.},
     year = {2019}
    }
    

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    T1  - Medical Image Compression Based on Combining Region Growing and Wavelet Transform
    AU  - Elnomery Allam Zanaty
    AU  - Sherif Mostafa Ibrahim
    Y1  - 2019/09/27
    PY  - 2019
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    DO  - 10.11648/j.ijmi.20190703.11
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
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    EP  - 65
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20190703.11
    AB  - Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.
    VL  - 7
    IS  - 3
    ER  - 

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