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Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression

by Sherif E. Hussein, Sherif M. Badr
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 3
Year of Publication: 2013
Authors: Sherif E. Hussein, Sherif M. Badr
10.5120/13839-1268

Sherif E. Hussein, Sherif M. Badr . Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression. International Journal of Computer Applications. 80, 3 ( October 2013), 9-15. DOI=10.5120/13839-1268

@article{ 10.5120/13839-1268,
author = { Sherif E. Hussein, Sherif M. Badr },
title = { Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 3 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number3/13839-1268/ },
doi = { 10.5120/13839-1268 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:33.027897+05:30
%A Sherif E. Hussein
%A Sherif M. Badr
%T Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 3
%P 9-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression became more significant for reducing data redundancy to save more hardware space and transmission bandwidth. Cloud computing on the other hand; provides elastic services, high performance and scalable data storage to a large and everyday increasing number of healthcare users. Today, clouds are mainly used for handling highly intensive computing workloads and for providing very large data storage facilities. Both goals are combined with a third goal of potentially reducing healthcare data storage cost. In this research, distributed cloud storage that can interact with many cloud providers was used as a backend while hybrid image compression/decompression technique was used in the front end.

References
  1. Hong Zhu, Weizhen Sun, Minhua Wu, Guixia Guan, Yong Guan "Pre-Processing of X-Ray Medical Image Based on Improved Temporal Recursive Self-Adaptive Filter," Young Computer Scientists, International Conference for, November 2008, pp. 758-763.
  2. S. Anand, R. Shantha Selva Kumari, S. Jeeva, T. Thivya "Directionlet transform based sharpening and enhancement of mammographic X-ray images," Biomedical Signal Processing and Control, Volume 8, Issue 4, July 2013, 391-399.
  3. J. Janet, Divya Mohandass and S. Meenalosini . Lossless Compression Techniques for Medical Images In Telemedicine. InTechOpen, Published on: 2011-03-16. DOI: 10. 5772/14399.
  4. Pablo Montero, Javier Taibo, Victor Gulías, Samuel Rivas, "Parallel Zigzag Scanning and Huffman Coding for a GPU-based MPEG-2 Encoder," ism, pp. 97-104, 2010 IEEE International Symposium on Multimedia, 2010.
  5. Sergio De Agostino, "Lempel-Ziv Data Compression on Parallel and Distributed Systems," ccp, pp. 193-202, 2011 First International Conference on Data Compression, Communications and Processing, 2011.
  6. Mustafa Safa Al-Wahaib, KokSheik Wong, "A Lossless Image Compression Algorithm Using Duplication Free Run-Length Coding," netapps, pp. 245-250, 2010 Second International Conference on Network Applications, Protocols and Services, 2010.
  7. I. Sodagar, B. -B. Chai, J. Wus, "A new error resilience technique for image compression using arithmetic coding," icassp, vol. 4, pp. 2127-2130, Acoustics, Speech, and Signal Processing, 2000 Vol 4. 2000 IEEE International Conference on, 2000.
  8. Cohen, L. D. (1991) 'On active contour models and balloons', Computer Vision, Graphics, and Image Processing. Image Understanding, Vol. 53, No. 2, pp. 211–218.
  9. Antonin Chambolle, Ronald A. DeVore, Nam-yong Lee, and Bradley J. Lucier. Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Process. , 7(3):319-335, 1998.
  10. Iyriboz, T. A. , Zukoski, M. J. , Hopper, K. D. and Stagg, P. L. (1999) 'A comparison of wavelet and joint photographic experts group lossy compression methods applied to medical images', Journal of Digital Imaging, May, Vol. 12, pp. 14–17.
  11. Yao-Tien Chen, Din-Chang Tseng "Wavelet-based medical image compression with adaptive prediction," Computerized Medical Imaging and Graphics, Pages 1–8, Volume 31, Issue 1, January 2007.
  12. Chen, C. W. , Zhang, Y. Q. , Luo, J. and Parker, K. J. (1995) 'Medical image compression with structure-preserving adaptive quantization', Visual Communication and Image Processing '95, Vol. 2501, No. 2, pp. 983–994.
  13. Yasser El-Sonbaty,Sherin M. Youssef,Karma M. Fathalla "Enhanced fuzzy-based models for ROI extraction in medical images," International Symposium on Signal Processing and Information Technology, December 2011, pp. 299-304
  14. Wen Sun , Yan Lu, Feng Wu, Shipeng Li "Level embedded medical image compression based on value of interest,"Proceedings of the 16th IEEE international conference on Image processing Pages 1749-1752, NJ, USA, 2009.
  15. Matthew J Zukoski; Terrance Boult; Tunç Iyriboz "A novel approach to medical image compression," International journal of bioinformatics research and applications 2(1):89-103, 2006.
  16. Storm, J. and Cosman, P. C. 'Medical image compression with lossless regions of interest', Signal Processing, pp. 155–171, Vol. 59, No. 2, 1997.
  17. Amnesh Goel, Nidhi Chandra, "A Prototype Model for Secure Storage of Medical Images and Method for Detail Analysis of Patient Records with PACS," csnt, pp. 167-170, 2012 International Conference on Communication Systems and Network Technologies, 11-13 May, 2012.
  18. Fatma E. -Z. A. Elgamal, Noha A. Hikal, F. E. Z. Abou-Chadi "Secure Medical Images Sharing over Cloud Computing environment," (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 5, 2013
  19. Jing Tian, Li Chen "Image despeckling using a non-parametric statistical model of wavelet coefficients," Biomedical Signal Processing and Control, Volume 6, Issue 4, October 2011, Pages 432-437.
  20. Collins TJ (July 2007). "ImageJ for microscopy". BioTechniques 43 (1 Suppl): 25–30. doi:10. 2144/000112517. PMID 17936939. Open Access logo PLoS transparent. svg.
  21. Eliceiri K, Rueden C (2005). "Tools for visualizing multidimensional images from living specimens". Photochem Photobiol 81 (5): 1116–22. doi:10. 1562/2004-11-22-IR-377. PMID 15807634.
  22. Barboriak D, Padua A, York G, Macfall J (2005). "Creation of DICOM—Aware Applications Using ImageJ". J Digit Imaging 18 (2): 91–9. doi:10. 1007/s10278-004-1879-4. PMC 3046706. PMID 15827831.
  23. Rajwa B, McNally H, Varadharajan P, Sturgis J, Robinson J (2004). "AFM/CLSM data visualization and comparison using an open-source toolkit". Microsc Res Tech 64 (2): 176–84. doi:10. 1002/jemt. 20067. PMID 15352089.
  24. Gering E, Atkinson C (2004). "A rapid method for counting nucleated erythrocytes on stained blood smears by digital image analysis". J Parasitol 90 (4): 879–81. doi:10. 1645/GE-222R. PMID 15357090.
  25. Michael J. Ackerman, Terry S. Yoo "Open source software for medical image processing and visualization," Communications of the ACM, February 2005, pp. 55-59.
  26. Sherif M Badr "Secured Hierarchically Dependent Distributed Database Model Applied to Hospitals Information System (HIS)", IJCA (0975 – 8887) Volume 66– No. 22, March 2013.
  27. M. Vrable, S. Savage, and G. M. Voelker. Cumulus: Filesystem backup to the cloud. Trans. Storage, 5(4):1–28, 2009. RACS: A Case for Cloud Storage Diversity.
  28. Qinlu He, Zhanhuai Li, Xiao Zhang "Study on Cloud Storage System Based on Distributed Storage Systems," Computational and Information Sciences, International Conference on, pp 1332-1335, December 2010.
  29. Jing Han,Meina Song,Junde Song "A Novel Solution of Distributed Memory NoSQL Database for Cloud Computing," Computer and Information Science, ACIS International Conference on, pp. 351-355, May 2011.
  30. Thanasis G. Papaioannou,Nicolas Bonvin,Karl Aberer "Scalia: An adaptive scheme for efficient multi-cloud storage," 2012 SC - International Conference for High Performance Computing, Networking, Storage and Analysis" pp. 1-10, November 2012.
  31. Hussam Abu-Libdeh, Lonnie Princehouse, Hakim Weatherspoon "RACS: a case for cloud storage diversity," SoCC '10 Proceedings of the 1st ACM symposium on Cloud computing, New York, NY, USA, 2010, pp. 229-240, ISBN: 978-1-4503-0036-0.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed cloud storage image compression healthcare data

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