We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8

by M. Ferni Ukrit, G. R. Suresh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 11
Year of Publication: 2014
Authors: M. Ferni Ukrit, G. R. Suresh
10.5120/15028-3344

M. Ferni Ukrit, G. R. Suresh . Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8. International Journal of Computer Applications. 86, 11 ( January 2014), 10-15. DOI=10.5120/15028-3344

@article{ 10.5120/15028-3344,
author = { M. Ferni Ukrit, G. R. Suresh },
title = { Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8 },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 11 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number11/15028-3344/ },
doi = { 10.5120/15028-3344 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:56.069165+05:30
%A M. Ferni Ukrit
%A G. R. Suresh
%T Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 11
%P 10-15
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The necessity in medical image compression continuously grows during the last decade. In advanced medical life large number of medical images is processed in hospitals and medical centers around the world. These images are in the form of sequences which are much correlated and are of great importance. Hence lossless image compression is needed to reproduce the original quality of the image without any loss of information. To exploit the correlation a new algorithm is proposed in this paper. The proposed compression method combines Super-Spatial Structure Prediction with motion estimation and motion compensation to achieve higher compression ratio. This is applied with a simple block-matching process Binary Tree Search. Results are compared in terms of Compression Ratio and Peak Signal-to-Noise Ratio. The proposed methodology provides better CR and PSNR than the other state-of-the-art algorithm.

References
  1. Shaou-Gang Miaou, Fu-Sheng Ke, and Shu-Ching Chen, "A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding," IEEE Transaction on Information Technology in Biomedicine, vol. 13,No. 5,Sep 2009
  2. S. E. Ghare, M. A. Mohd . Ali, K. Jumari and M. Ismail, "An Efficient Low Complexity Lossless Coding Algorithm for Medical Images," in American Journal of Applied Sciences 6 (8): 1502-1508, 2009.
  3. R. Srikanth, A. G. Ramakrishnan, "Context- Interframe Coding of MR Images".
  4. S. Bhavani, Dr. K. Thanushkodi, "A Survey in Coding Algorithms in Medical Image Compression,"in International Journal on Computer Science and Engineering, vol. 02, No. 5, 2010, 1429-1434
  5. Xiwen OwenZhao,Zhi haiHenryHe, "Lossless Image Compression Using Super-Spatial Structure Prediction",IEEE Signal Processing,vol. 17,no. 4,April 2010
  6. C. S. Rawat, Seema G Bhatea, Dr. Sukadev Meher,"A Novel Algoritm of Super-Spatial Structure Prediction for RGB Colourspace",International ournal of Scientific & Engineering Research, vol. 3, Issue 2, February 2012.
  7. M. J. Weinberger, G. Seroussi, and G. Sapiro, "The LOCO-I Lossless Image Compression Algorithm: Principles and standardization into JPEG-LS," IEEE Trans. Image Process. , vol. 9, no. 8, pp. 1309–1324, Aug. 2000.
  8. M. Weinberger, G. Seroussi, and G. Sapiro, "LOCO-I: A Low Complexity, Context-based, Lossless Image Compression Algorithm," in Proc. IEEE Data Compression Conf. , Snowbird,UT, Mar. /Apr. 1996, pp. 140–149.
  9. Y. D. Wang, "The Implementation of Undistorted Dynamic Compression Technique for Biomedical Image," Master's thesis, Dept. Electr. Eng. , Nat. Cheng Kung Univ. , Taiwan, 2005.
  10. D. Brunello, G. Calvagno, G. A. Mian, and R. Rinaldo, "Lossless Compression of Video using Temporal Information," IEEE Trans. Image Process, vol. 12, no. 2, pp. 132–139, Feb. 2003.
  11. N. D. Memon and Khalid Sayood, "Lossless Compression of Video Sequences," IEEE Trans. Commun. , vol. 44, no. 10, pp. 1340-1345.
  12. M. F. Zhang, J. Hu, and L. M. Zhang, "Lossless Video Compression using Combination of Temporal and Spatial Prediction," in Proc. IEEE Int. Conf. Neural Newt. Signal Process. Dec. 2003, vol. 2, pp. 1193–1196.
  13. Mudassar Raza, Ahmed Adnan et. al, "Lossless Compression Method for Medical Image Sequences Using Super-Spatial Structure Prediction and Inter-frame Coding," International Journal of Advanced TResearch and Technology, vol. 10,No. 4,August 2012.
  14. C. Saravanan and M. Surrendar, "Enhancing Efficiency of Huffman Coding Using Lempel-Ziv Coding for Image Compression," International Journal of Soft Computing and Engineering, ISSN:2231-2307, Vol. 2, Issue-6, January 2011.
  15. T. Wiegand, G. J. Sullivan et. al. , "Overview of the H. 264/AVC video coding standard," IEEE Trans. Circuits Systems Video Technology, vol. 13,No. 7,June 2003.
  16. Juan,Diego et. al. , "Pseudo-periodic surrpgate data method on voice signals," 11th International Conference, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Medical Image Sequences Super-Spatial Structure Prediction Lossless Compression Motion Estimation and Motion Compensation Inter-frame Coding CALIC LZ8