International Journal of Medical Imaging

| Peer-Reviewed |

An Effective Method for e-Medical Data Compression Using Wavelet Analysis

Received: Oct. 28, 2018    Accepted: Nov. 13, 2018    Published: Dec. 20, 2018
Views:       Downloads:

Share This Article

Abstract

The continuous utilization of massive patient data via telecommunication medium is raising a concern either in data transmission speed, storage, security and privacy. The introduction of Informatization, Internet of Things (IoT), Big Data Technology, and e-health require effective data compression techniques that will help solve the numerous challenges evident in the conventional medical image compression schemes. In order to successfully transmit medical data via the network of networks demands an efficient data compression mechanisms without reduction in the image quality with reduced size. This mechanism greatly minimizes costs, provides mobility and comfort to the users, increase speed in medical file transmission and lot of more. The research investigates the various medical image compression platforms so, as to achieve efficient and effective scheme. Medical image compression require more proactive scheme that maintains vital features of patients. Several compression methods were applied and Discrete Cosine Transform (DCT) proved to have a superior compression ratio as opposed to Discrete Wavelet Transform (DWT). The proposed study indicated that the recovered medical images had similar results compared to the original image data. Finally, the research mitigated data storage issue of hard drive, reduce transmission time, improved patient’s mobility and the high cost of medical hardware devices.

DOI 10.11648/j.ijmi.20180603.12
Published in International Journal of Medical Imaging ( Volume 6, Issue 3, September 2018 )
Page(s) 25-32
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

Wavelet Transform, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT)

References
[1] Stephen Wong, Loren Zaremba, David Gooden, and H. K. Huang” Radiologic Image Compression-A Review.
[2] S. Hludov, Chr. Meinel Institut of Telematics,” DICOM - image compression.
[3] S. Sagiroglu, D. Sinanc. Big data: A review [C]. International Conference on Collaboration Technologies and Systems, 2013, 42–47.
[4] M. Molly Knapp. Big Data. Journal of Electronic Resources in Medical Libraries [J]. 2013, 10 (4), 215–222.
[5] F. F. Costa. Big data in biomedicine [J]. Drug Discovery Today, 2014, 19 (4), 433–440.
[6] Ms. Sonam Malik and Mr. Vikram Verma’ Comparative analysis of DCT, Haar and Daubechies Wavelet for Image Compression’ Student, Dept. of Electronics & Communication JMIT / Radaur /India, Assistant Professor, Deptt. Of I. T. JMIT / Radaur, India.
[7] DCT-BASED IMAGE COMPRESSION by Vision Research and Image Sciences Laboratory.
[8] Andrew B. Watson, NASA Ames Research Center, Image Compression Using the Discrete Cosine Transform, Mathematica Journal, 4 (1), 1994, p. 81-88.
[9] Compression of Medical Images Using Wavelet Transforms- Ruchika, Mooninder Singh, Anant Raj Singh
[10] M. Antonini, et al.: “Image Coding Using Wavelet Transforms” IEEE Trans. Image Processing, vol. 1, no. 2, pp. 205-220, April 1992.
[11] V. Marx, Biology. The big challenges of big data [J]. Nature, 2013, 498 (7453), 255
[12] Liang, Z. P.; Lauterbur, P. C. Principles of Magnetic Resonance Imaging: A Signal Processing Perspective; Wiley-IEEE Press: New York, NY, USA, 1999.
[13] Z. Zhang and B. D. Rao, “Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation,” IEEE Trans. on Signal Processing, vol. 61, no. 8, pp. 2009–2015, 2013.
[14] ISO/IEC 15444-1 j ITU-T Rec. T.800, Information Technology - JPEG 2000 Image Coding System: Core Coding System, 2002.
[15] P. Schelkens, A. Skodras, T. Ebrahimi, The JPEG 2000 Suite, Wiley Publishing, 2009.
[16] V. Sanchez, J. Bartrina-Rapesta, Lossless compression of medical images based on HEVC intra coding., in: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014, Florence, Italy, May 4–9, 2014, 2014, pp. 6622–6626.
[17] V. Sanchez, F. A. Llinàs, J. Bartrina-Rapesta, J. Serra- Sagristà, Improvements to HEVC intra coding for lossless medical image compression, in: Data Compression Conference, DCC 2014, Snowbird, UT, USA, 26– 28 March, 2014, p. 423.
[18] W. B. Pennebarker, J. L. Mitchell, JPEG still Image Data Compression Standard, 1st edition, Kluwer Academic Publisher 1992.
[19] ISO/IEC 10918-1 j ITU-T Rec. T.81, Information Technology – Digital Compression and Coding of Continuous-tone Still Images, 1992.
[20] J. Walker and T. Nguyen. Wavelet-based image compression [J]. 2001
[21] S. Grgic, M. Grgic, B. Zovko-Cihlar. Performance analysis of image compression using wavelets [J].2001, 48 (3), 682–695
[22] Said, A., & Pearlman, W. A. (to appear). An image multiresolution representation for lossless and lossy compression. IEEE Transactions on Image Processing.
[23] R. C. Gonzalez, R. E. Woods, S. L. Eddins, ―Digital Image Processing using MATLAB‖.
[24] Pu, L.; Marcellin, M. W.; Bilgin, A.; Ashok, A. Image compression based on task-specific information. In Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, 27–30 October 2014; pp. 4817–4821.
[25] M. Kaur, G. Kaur. A Survey of Lossless and Lossy Image Compression Techniques [J]. 2013,3(2), 323–326
[26] Ibrahim Abdulai Sawaneh: A DWT Image Based Compression for Health Systems. P. 33, July 2017.
Cite This Article
  • APA Style

    Ibrahim Abdulai Sawaneh. (2018). An Effective Method for e-Medical Data Compression Using Wavelet Analysis. International Journal of Medical Imaging, 6(3), 25-32. https://doi.org/10.11648/j.ijmi.20180603.12

    Copy | Download

    ACS Style

    Ibrahim Abdulai Sawaneh. An Effective Method for e-Medical Data Compression Using Wavelet Analysis. Int. J. Med. Imaging 2018, 6(3), 25-32. doi: 10.11648/j.ijmi.20180603.12

    Copy | Download

    AMA Style

    Ibrahim Abdulai Sawaneh. An Effective Method for e-Medical Data Compression Using Wavelet Analysis. Int J Med Imaging. 2018;6(3):25-32. doi: 10.11648/j.ijmi.20180603.12

    Copy | Download

  • @article{10.11648/j.ijmi.20180603.12,
      author = {Ibrahim Abdulai Sawaneh},
      title = {An Effective Method for e-Medical Data Compression Using Wavelet Analysis},
      journal = {International Journal of Medical Imaging},
      volume = {6},
      number = {3},
      pages = {25-32},
      doi = {10.11648/j.ijmi.20180603.12},
      url = {https://doi.org/10.11648/j.ijmi.20180603.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijmi.20180603.12},
      abstract = {The continuous utilization of massive patient data via telecommunication medium is raising a concern either in data transmission speed, storage, security and privacy. The introduction of Informatization, Internet of Things (IoT), Big Data Technology, and e-health require effective data compression techniques that will help solve the numerous challenges evident in the conventional medical image compression schemes. In order to successfully transmit medical data via the network of networks demands an efficient data compression mechanisms without reduction in the image quality with reduced size. This mechanism greatly minimizes costs, provides mobility and comfort to the users, increase speed in medical file transmission and lot of more. The research investigates the various medical image compression platforms so, as to achieve efficient and effective scheme. Medical image compression require more proactive scheme that maintains vital features of patients. Several compression methods were applied and Discrete Cosine Transform (DCT) proved to have a superior compression ratio as opposed to Discrete Wavelet Transform (DWT). The proposed study indicated that the recovered medical images had similar results compared to the original image data. Finally, the research mitigated data storage issue of hard drive, reduce transmission time, improved patient’s mobility and the high cost of medical hardware devices.},
     year = {2018}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - An Effective Method for e-Medical Data Compression Using Wavelet Analysis
    AU  - Ibrahim Abdulai Sawaneh
    Y1  - 2018/12/20
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijmi.20180603.12
    DO  - 10.11648/j.ijmi.20180603.12
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
    SP  - 25
    EP  - 32
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20180603.12
    AB  - The continuous utilization of massive patient data via telecommunication medium is raising a concern either in data transmission speed, storage, security and privacy. The introduction of Informatization, Internet of Things (IoT), Big Data Technology, and e-health require effective data compression techniques that will help solve the numerous challenges evident in the conventional medical image compression schemes. In order to successfully transmit medical data via the network of networks demands an efficient data compression mechanisms without reduction in the image quality with reduced size. This mechanism greatly minimizes costs, provides mobility and comfort to the users, increase speed in medical file transmission and lot of more. The research investigates the various medical image compression platforms so, as to achieve efficient and effective scheme. Medical image compression require more proactive scheme that maintains vital features of patients. Several compression methods were applied and Discrete Cosine Transform (DCT) proved to have a superior compression ratio as opposed to Discrete Wavelet Transform (DWT). The proposed study indicated that the recovered medical images had similar results compared to the original image data. Finally, the research mitigated data storage issue of hard drive, reduce transmission time, improved patient’s mobility and the high cost of medical hardware devices.
    VL  - 6
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Department of Computer Science, Institute of Advanced Management and Technology (IAMTECH), Freetown, Sierra Leone

  • Section