CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Computational Modelling of Image Coding using ROI based Medical Image Compression

by Suma, V Sridhar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 5
Year of Publication: 2014
Authors: Suma, V Sridhar
10.5120/18908-0206

Suma, V Sridhar . Computational Modelling of Image Coding using ROI based Medical Image Compression. International Journal of Computer Applications. 108, 5 ( December 2014), 20-27. DOI=10.5120/18908-0206

@article{ 10.5120/18908-0206,
author = { Suma, V Sridhar },
title = { Computational Modelling of Image Coding using ROI based Medical Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 5 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number5/18908-0206/ },
doi = { 10.5120/18908-0206 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:43:34.546354+05:30
%A Suma
%A V Sridhar
%T Computational Modelling of Image Coding using ROI based Medical Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 5
%P 20-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the age of digital medical imaging communication and robotic transmission of real-time image for robot guided operations in constrained bandwidth is a challenging task. The issue of compression, in medical images, is the prime focus of this study. The study has aimed to perform an investigation on the frequently adopted region-of-interest scheme called as MAXSHIFT. The design principle of this standard encoding technique allows encoding and highly prioritizing only the region of interest and then emphasis on the background (non-region of interest area). The system allows the deployment of multiple and randomly shaped region of interest within the medical images using randomized weights for emphasizing each element of ROI. Supported by the discussion on some of the prior research work, and how this study is motivated, the present manuscript illustrates the experimental phases of implementing MAXSHIFT on two dimensional medical images. In order to check the robustness of the algorithm, the performance parameters such as bit per pixel (BPP) and Signal to Noise Ratio (SNR) is being evaluated on enormous medical images.

References
  1. Radha, V. 2011. A Comparative Study on ROI-Based Lossy Compression Techniques for Compressing Medical Image. Proceedings of the World Congress on Engineering and Computer Science, Vol I
  2. Gokturk,S. B. , Tomasi. C. , Girod, B, Beaulieu, C. 2001. Medical image compression based on region of interest, with application to colon CT images. Engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE, Vol. 3, No. , pp. 2453-2456
  3. Loganathan, R. , Kumaraswamy, Y. S. 2012. Medical image compression with lossless region of interest using adaptive active contour. Journal of Computer Science, Vol. 8, pp. 747-751
  4. Babu, D. , Vijendra, D. N. , Alamelu. 2009. Wavelet Based Medical Image Compression Using ROI EZW. International Journal of Recent Trends in Engineering, Vol. 1, No. 3, pp. 97-100
  5. Bairagi, V. K. , Sapkal, A. M. 2013. ROI-based DICOM image compression for telemedicine. Vol. 38, pp. 123-131
  6. Sridevi, M. E. , Vijayakuymar, V. R. , Anuja, R. 2012. A Survey on Various Compression Methods for Medical Images. International Journal of Intelligent Systems and Applications (IJISA), Vol 4, No. 3
  7. Wakatani, A. 2002. Digital watermarking for ROI medical images by using compressed signature image. System Sciences, Proceedings of the 35th Annual Hawaii International Conference
  8. Kumar, S. , Goel, N. , Singh, V. , Choudhary, A. , Sorohi, N. , Singh, G. 2013. Fast and Efficient Medical Image Compression Using Contourlet Transform:(FEMI-CCT). Open Journal of Computer Sciences, Vol. 1, No. 1
  9. Thomas, D. S. , Moorthi, M. , Muthalagu, R. 2014. Medical Image Compression based on Automated ROI Selection for Telemedicine Application. International Journal Of Engineering And Computer Science. Vol. 3, Issue. 1
  10. Chavad, P. , Patel, N. , Patel, K. 2014. Region of Interest Based Image Compression. International Journal of Innovative Research in Computer and Communication Engineering. Vol. 2, Issue. 1
  11. Naidu, V. R. , Agrawal, V. K. , Ramakrishnan, A. G. 2008. MR image compression with ROI decoding, SNR and Spatial scalability, In proceeding of: IISc Centenary Conference
  12. Arun, R. , Murugan, D. 2012. Medical Image Compression Using Region Growing Segmentation. International Journal Of Computational Engineering Research. Vol. 2, Issue. 2
  13. Kaur, B. , Aggarwal, D. , Kaur, G. , Kaur, A. 2011. Efficient Image Compression based on Region of Interest, International Journal of Computer Science and Telecommunications, Vol. 2, Issue. 1
  14. Sridevi, S. , Vijayakumar, V. R. 2013. Contextual Region of Interest Based Medical Image Compression using Contextual Listless SPIHT Algorithm for Brain Images. International Journal of Engineering & Technology, pp. 0975-4024
  15. Lavanya, M. , Kumar, S. 2013. Intelligent Compression of Medical Images Based on Multi ROI. International Journal of Emerging Technology and Advanced Engineering, Vol. 3, Issue. 1
  16. Mateika, D. , Martaviciu, R. 2006. Analysis of the Compression Ratio and Quality in Medical Images. Information Technology and Control, Vol. 35, No. 4
  17. N-A, Amine. , C-M. Christine2013. Compression of Biomedical Images and Signals, John Wiley & Sons Science
  18. Beutel, J. 2000. Handbook of Medical Imaging: Display and PACS, SPIE Press, pp. 512
  19. Penedo, M. . , Pearlman, W. A. , Tahoces, P. G. , Souto, M. , Vidal, J. J. 2003. Region-based wavelet coding methods for digital mammography. IEEE Trans Med Imaging. Vol. 22
  20. Silva, V. F. 2010. Advances in medical image compression: Novel schemes for highly efficient storage, transmission and on demand scalable access for 3D and 4D medical imaging data. Doctorial Thesis, The University of British Columbia
  21. Anastassopoulos, G. K and Skodras, A. N. 2002. JPEG2000 ROI Coding In Medical Imaging Applications. Proc. 2nd LASTED Int. Conf. On Visualisation, Imaging and Image Processing, pp. 783–788
  22. Chen, Y-T. , Tseng, D-C and Chang, P-C. 2006. Wavelet-Based Image Compression with Polygon-Shaped Region of Interest, Springer, pp. 878 – 887
  23. Pawadshetty, J. S. , Bakal, J. W. 2013. JPEG 2000 Region of Interest Coding Methods. International Journal of Engineering Research and Applications, Vol. 3, Issue. 1, pp. 1184-1188
  24. http://www. via. cornell. edu/
  25. Doukas, C. , Maglogiannis, I. 2007. Region of Interest Coding Techniques for Medical Image Compression. IEEE Engineering In Medicine And Biology Magazine
  26. Suma and V. Sridhar. 2014. Article: A Review of the Effective Techniques of Compression in Medical Image Processing. International Journal of Computer Applications, Vol. 97(6), pp. 23-30
Index Terms

Computer Science
Information Sciences

Keywords

Medical Images Compression JPEG2000 MAXSHIFT Region of Interest

Powered by PhDFocusTM