Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Elnomery Allam Zanaty; Sherif Mostafa Ibrahim

doi:doi:10.11648/j.ijmi.20190703.11

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Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Elnomery Allam Zanaty, Sherif Mostafa Ibrahim

Published in International Journal of Medical Imaging (Volume 7, Issue 3)

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.

Published in	International Journal of Medical Imaging (Volume 7, Issue 3)
DOI	10.11648/j.ijmi.20190703.11
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), 2019. Published by Science Publishing Group

Keywords

Wavelets Transform, Region Growing, SPIHT, Daubechies, Biorthogonal

References

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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://article.sciencepublishinggroup.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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TY - JOUR
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
N1 - https://doi.org/10.11648/j.ijmi.20190703.11
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
SP - 57
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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Author Information

Elnomery Allam Zanaty

Faculty of Computers & Information, Sohag University, Sohag, Egypt
Sherif Mostafa Ibrahim

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

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Plain Text BibTeX RIS

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://article.sciencepublishinggroup.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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