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Reseach Article

Review on Region of Interest Coding Techniques for Medical Image Compression

by Palak Jangbari, Dhruti Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 10
Year of Publication: 2016
Authors: Palak Jangbari, Dhruti Patel
10.5120/ijca2016907859

Palak Jangbari, Dhruti Patel . Review on Region of Interest Coding Techniques for Medical Image Compression. International Journal of Computer Applications. 134, 10 ( January 2016), 1-5. DOI=10.5120/ijca2016907859

@article{ 10.5120/ijca2016907859,
author = { Palak Jangbari, Dhruti Patel },
title = { Review on Region of Interest Coding Techniques for Medical Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 10 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number10/23947-2016907859/ },
doi = { 10.5120/ijca2016907859 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:20.214866+05:30
%A Palak Jangbari
%A Dhruti Patel
%T Review on Region of Interest Coding Techniques for Medical Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 10
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Telemedicine is a field of medicine characterized by telecommunication for clinical health care and transmission of medical images and videos. For transmission, a huge bandwidth is required over the internet. The size of the images that belongs to a single patient is very large which contains resolution factor and diagnostic images. So there is a need for efficient compression techniques for compressing these medical images.The regions which are considered to be more important than others in medical images is known as a Region of Interest (ROI) e.g. tumor is ROI in brain MRI. In this work, the ROI is detected by the saliency map technique, after that targets or ROIs be coded at available bits while the remainder of the background or non-ROI part is coded using fewer bits.By this method, the target regions within the video frame or image will be well preserved while the number of bits needed to code the video sequence or images is reduced.Thus, the transmission bandwidth and storage requirements are reduced.

References
  1. Xiaodi Hou and Liqing Zhang, “Saliency Detection: A Spectral Residual Approach,” IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp.1-8.
  2. Onsy Abdel Alim1, Nadder Hamdy and Wesam Gamal El-Din,“Determination of the Region of Interest in the Compression of Biomedical Images,” 24th National Radio Science Conference,2007,pp.1-6.
  3. Miaou S G, Ke F S and Chen S C,“A lossless compression method for medical image sequences using JPEG-LS and interframe coding.” IEEE Trans. Inform. Technol. Biomed.,2009, 13(5): 818–821.
  4. Maglogiannis I and Kormentzas G,“Wavelet-based compression with ROI coding support for mobile access to DICOM images over heterogeneous radio networks.” Trans. Inform. Technol. Biomed,2009, 13(4):458–466.
  5. S.R. Kodituwakku, U. S.Amarasinghe, “Comparision of lossless datacompression algorithms for text data”, ISSN : 0976-5166,2010,vol 1,pp. 416-425.
  6. D.Wu and E.C.Tan, “Comparison of lossless image compression algorithms,” IEEE conference on TENCON 1999, vol.1, pp.718 - 721.
  7. ISO/IEC JTC 1/SC 29/WG 1 (ITU-T SG8), JPEG 2000 Part II Final Committee Draft, Dec. 2000.
  8. ISO/IEC JTC 1/SC 29/WG 1 (ITU-T SG8), JPEG 2000 Part I Final Committee Draft, Version 1.0,Mar. 2000.
  9. Z. Liu, J. Ha, Z. Xiong, Q. Wu, and K. Castleman, “Lossy-to-lossless ROI coding of chromosome images using modified SPIHT and EBCOT,” in Proc. IEEE Int. Symp. Biomedical Imaging, Washington, DC, 2002,p. 317.
  10. A. Said and W.A. Pearlman, “A new, fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol. 1996, vol. 6 ,pp. 243–250.
  11. D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans. Image Processing,2000, vol. 9, pp. 1158–1170
  12. Z. Liu, J. Ha, Z. Xiong, Q. Wu, and K. Castleman, “Cascaded differential and wavelet compression of chromosome images,” IEEE Trans. Biomed. Eng.,2002, vol. 49, pp. 323–283.
  13. G. Minami, Z. Xiong, A. Wang, and S. Mehrotra, “3-D wavelet coding of video with arbitrary regions of support,” IEEE Trans. Circuits Syst. VideoTechnol., 2001,vol. 11, pp. 1063–1068.
  14. S. Tasdoken and A. Cuhadar, “ROI coding with integer wavelet transforms and unbalanced spatial orientation trees,” presented at 25th Ann. Int. Conf. IEEE EMBS, Mexico, Sept. 2003.
  15. S. Dewitte and J. Cornelis, “Lossless integer wavelet transform,” IEEE Signal Processing Lett.,1997, vol. 4, pp.158–160.
  16. M. Penedo, W.A. Pearlman, P.G. Tahoces, M. Souto, and J.J. Vidal,“Region-based wavelet coding methods for digital mammography,” IEEE Trans. Med. Imag., Oct. 2003, vol. 22, pp.1288–1296.
  17. S. Li and W. Li, “Shape-adaptive discrete wavelet transform for arbitrary shaped visual object coding,” IEEE Trans. Circuits Syst. Video Technol., Aug. 2000,vol. 10, pp. 725–743.
  18. A. Islam and W.A. Pearlman, “An embedded and efficient low-complexity hierarchical image coder,” in Proc. SPIE, Dec. 1999, vol. 3653, p. 294–305.
  19. R. Dilmaghani, A. Ahmadian, M. Ghavami, M. Oghabian, and H.Aghvami, “Multi rate/resolution control in progressive medical image transmission for the region of interest (ROI) using EZW,” presented at the 25thAnn. Int. Conf. IEEE EMBS, Mexico, Sept. 2003.
  20. A. Cziho, G. Cazuguel, B. Solaiman, and C. Roux, “Medical imagecompression using region-of-interest vector quantization,” in Proc. 20th Ann. Int. Conf. IEEE EMBS, Hong Kong, 1998,p. 1277.
  21. I. Ueno and W. Pearlman, “Region of interest coding in volumetricimages with shape-adaptive wavelet transform,” in Proc. SPIE, May 2003,vol. 5022,pp. 1048–1055.
  22. S.B. Gokturk, C. Tomasi, B. Girod, and C. Beaulieu, “Medical image compression based on region of interest, with application to colon CT images,” in Proc. 23rd Ann. Int. Conf. IEEE EMBS, Istanbul,2001, p. 2453.
  23. Z. Xiong, X. Wu, and D.Y. Yun, “Progressive coding of medical volumetric data using three-dimensional integer wavelet packet transform,” in Proc. 2nd IEEE Workshop Multimedia Signal Processing, Redondo Beach,CA, Dec. 1998, pp. 553–558.
  24. G. Bernabe, J. Gonzalez, J.M. Garcia, and J. Duato, “A new lossy 3-D wavelet transform for high-quality compression of medical video,” in Proc. IEEE EMBS Int. Conf. Information Technology Applications in Biomedicine,Arlington, VA, Sept. 2000, p. 226.
  25. D. Gibson, M. Spann, and S.I. Woolley, “A wavelet-based region of interest encoder for the compression of angiogram video sequences,” IEEE Trans. Inform. Technol. Biomed., Jun. 2004,vol. 8, pp. 103–113.
  26. Bairagi, V.K.; Sapkal, A.M., "Automated region-based hybrid compression for digital imaging and communications in medicine magnetic resonance imaging images for telemedicine applications," in Science, Measurement & Technology, IET ,July 2012, vol.6, no.4, pp.247-253.
  27. Lan, Xuguang, Nanning Zheng, Wen Ma, and Yuan Yuan. "Arbitrary ROI-based wavelet video coding." Neurocomputing 74, 2011,no.12,pp. 2114-2122.
  28. Bairagi, Vinayak K., and Ashok M. Sapkal. "ROI-based DICOM image compression for telemedicine."  Sadhana 38,2013, no. 1,pp. 123-131.
  29. Doukas, C., Maglogiannis, I.: ‘Region of interest coding techniques for medical image compression’, IEEE Eng. Med. Biol. Mag., 2007, 26, (5),pp. 29–35.
Index Terms

Computer Science
Information Sciences

Keywords

Compression Region of Interest (ROI) Saliency Map