Volume 6, Issue 3, September 2018, Page: 25-32
An Effective Method for e-Medical Data Compression Using Wavelet Analysis
Ibrahim Abdulai Sawaneh, Department of Computer Science, Institute of Advanced Management and Technology (IAMTECH), Freetown, Sierra Leone
Received: Oct. 28, 2018;       Accepted: Nov. 13, 2018;       Published: Dec. 20, 2018
DOI: 10.11648/j.ijmi.20180603.12      View  850      Downloads  83
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.
Wavelet Transform, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT)
To cite this article
Ibrahim Abdulai Sawaneh, An Effective Method for e-Medical Data Compression Using Wavelet Analysis, International Journal of Medical Imaging. Vol. 6, No. 3, 2018, pp. 25-32. doi: 10.11648/j.ijmi.20180603.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Stephen Wong, Loren Zaremba, David Gooden, and H. K. Huang” Radiologic Image Compression-A Review.
S. Hludov, Chr. Meinel Institut of Telematics,” DICOM - image compression.
S. Sagiroglu, D. Sinanc. Big data: A review [C]. International Conference on Collaboration Technologies and Systems, 2013, 42–47.
M. Molly Knapp. Big Data. Journal of Electronic Resources in Medical Libraries [J]. 2013, 10 (4), 215–222.
F. F. Costa. Big data in biomedicine [J]. Drug Discovery Today, 2014, 19 (4), 433–440.
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.
DCT-BASED IMAGE COMPRESSION by Vision Research and Image Sciences Laboratory.
Andrew B. Watson, NASA Ames Research Center, Image Compression Using the Discrete Cosine Transform, Mathematica Journal, 4 (1), 1994, p. 81-88.
Compression of Medical Images Using Wavelet Transforms- Ruchika, Mooninder Singh, Anant Raj Singh
M. Antonini, et al.: “Image Coding Using Wavelet Transforms” IEEE Trans. Image Processing, vol. 1, no. 2, pp. 205-220, April 1992.
V. Marx, Biology. The big challenges of big data [J]. Nature, 2013, 498 (7453), 255
Liang, Z. P.; Lauterbur, P. C. Principles of Magnetic Resonance Imaging: A Signal Processing Perspective; Wiley-IEEE Press: New York, NY, USA, 1999.
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.
ISO/IEC 15444-1 j ITU-T Rec. T.800, Information Technology - JPEG 2000 Image Coding System: Core Coding System, 2002.
P. Schelkens, A. Skodras, T. Ebrahimi, The JPEG 2000 Suite, Wiley Publishing, 2009.
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.
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.
W. B. Pennebarker, J. L. Mitchell, JPEG still Image Data Compression Standard, 1st edition, Kluwer Academic Publisher 1992.
ISO/IEC 10918-1 j ITU-T Rec. T.81, Information Technology – Digital Compression and Coding of Continuous-tone Still Images, 1992.
J. Walker and T. Nguyen. Wavelet-based image compression [J]. 2001
S. Grgic, M. Grgic, B. Zovko-Cihlar. Performance analysis of image compression using wavelets [J].2001, 48 (3), 682–695
Said, A., & Pearlman, W. A. (to appear). An image multiresolution representation for lossless and lossy compression. IEEE Transactions on Image Processing.
R. C. Gonzalez, R. E. Woods, S. L. Eddins, ―Digital Image Processing using MATLAB‖.
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.
M. Kaur, G. Kaur. A Survey of Lossless and Lossy Image Compression Techniques [J]. 2013,3(2), 323–326
Ibrahim Abdulai Sawaneh: A DWT Image Based Compression for Health Systems. P. 33, July 2017.
Browse journals by subject