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Medical Image Compression using Adaptive Prediction and Block based Entropy Coding

by Ekta Ashok Baware, Jagruti Save
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 9
Year of Publication: 2016
Authors: Ekta Ashok Baware, Jagruti Save
10.5120/ijca2016912147

Ekta Ashok Baware, Jagruti Save . Medical Image Compression using Adaptive Prediction and Block based Entropy Coding. International Journal of Computer Applications. 153, 9 ( Nov 2016), 28-33. DOI=10.5120/ijca2016912147

@article{ 10.5120/ijca2016912147,
author = { Ekta Ashok Baware, Jagruti Save },
title = { Medical Image Compression using Adaptive Prediction and Block based Entropy Coding },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 9 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number9/26433-2016912147/ },
doi = { 10.5120/ijca2016912147 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:42.456203+05:30
%A Ekta Ashok Baware
%A Jagruti Save
%T Medical Image Compression using Adaptive Prediction and Block based Entropy Coding
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 9
%P 28-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Evolution of medical imaging has turned out as a boon for medical industry as it provides efficient diagnosis and monitoring of diseases. Compression of medical images helps accommodation of large medical data in limited storage space and fast transmission. The main aim of this paper is to compress medical images with no loss of clinical data using a lossless and adaptive prediction technique. The paper presents a prediction scheme adaptive to gradients defined in four directions. The proposed prediction scheme is based on the idea that the causal pixel in the direction of least gradient value contributes maximum in prediction. Before entropy encoding, the residual errors obtained are grouped on the basis of maxplane coding which further enhances coding efficiency. The proposed work is compared with basic DPCM technique and state of the art CALIC scheme. Experimental results show compression ratio for proposed method for medical images on average is 9.65% and 30.38% better than the CALIC scheme and basic DPCM method respectively while bit rates for proposed method is 6.51% and 30.86% better than CALIC scheme and DPCM method respectively.

References
  1. D. Theckedatth, “Introduction to Image Processing,” in Image Processing using MATLAB codes, 5th ed., India.
  2. S. B. Gokturk, C. Tomasi, B. Girod and C. Beaulieu, "Medical image compression based on region of interest, with application to colon CT images," Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, 2001, pp. 2453-2456 vol.3.
  3. S. Wong, L. Zaremba, D. Gooden and H. K. Huang, "Radiologic image compression-a review," in Proceedings of the IEEE, vol. 83, no. 2, pp. 194-219, Feb 1995.
  4. I. Daubechies, "Orthonormal bases of compactly supported Wavelets," Commun. Pure Appl Math., vol 41, pp. 909-996, Nov 1988.
  5. S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, Jul 1989.
  6. Yao-Tien Chen, Din-Chang Tseng and Pao-Chi Chang, "Wavelet-based medical image compression with adaptive prediction," 2005 International Symposium on Intelligent Signal Processing and Communication Systems, 2005, pp. 825-828.
  7. J. Rissanen, "Universal coding, information, prediction, and estimation," in IEEE Transactions on Information Theory, vol. 30, no. 4, pp. 629-636, Jul 1984.
  8. M. J. Weinberger, G. Seroussi and G. Sapiro, "LOCO-I: a low complexity, context-based, lossless image compression algorithm," Data Compression Conference, 1996. DCC '96. Proceedings, Snowbird, UT, 1996, pp. 140-149.
  9. Xiaolin Wu and N. Memon, "CALIC-a context based adaptive lossless image codec," Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, Atlanta, GA, 1996, pp. 1890-1893 vol. 4.
  10. S. Golomb, "Run-length encodings (Corresp.)," in IEEE Transactions on Information Theory, vol. 12, no. 3, pp. 399-401, Jul 1966.
  11. D. A. Huffman, "A Method for the Construction of Minimum-Redundancy Codes," in Proceedings of the IRE, vol. 40, no. 9, pp. 1098-1101, Sept. 1952.
  12. G. Langdon and J. Rissanen, "Compression of Black-White Images with Arithmetic Coding," in IEEE Transactions on Communications, vol. 29, no. 6, pp. 858-867, Jun 1981.
  13. A. Moffat, R. Neal and I. H. Witten, "Arithmetic coding revisited," Data Compression Conference, 1995. DCC '95. Proceedings, Snowbird, UT, 1995, pp. 202-211.
  14. A. Moffat, R. Neal and I. H. Witten, "Arithmetic coding revisited," Data Compression Conference, 1995. DCC '95. Proceedings, Snowbird, UT, 1995, pp. 202-211.
  15. N.D. Memon and K. Sayood, "Lossless image compression: A comparative study," in Proceedings of SPIE, vol. 2148, pp. 8–20, March 1995.
  16. B. Carpentieri, M. J. Weinberger and G. Seroussi, "Lossless compression of continuous-tone images," in Proceedings of the IEEE, vol. 88, no. 11, pp. 1797-1809, Nov. 2000.
  17. G. Langdon, A. Gulati and E. Seiler, "On the JPEG model for lossless image compression," in Data Compression Conference, 1992. DCC '92., Snowbird, UT, USA, 1992, pp. 172-180.
  18. X. Wu and N. Memon, "Context-based, adaptive, lossless image coding," in IEEE Transactions on Communications, vol. 45, no. 4, pp. 437-444, Apr 1997.
  19. H. Hu, "A Study of CALIC," M.S. Dissertation, Dept. of Computer Science and Electrical Engg., Maryland Univ., Baltimore County, Dec 2004.
  20. S. M. Guo, C. Y. Hsu and J. S. H. Tsai, "Efficient image compression based on error value centralization by sign bits," TENCON 2013 - 2013 IEEE Region 10 Conference (31194), Xi'an, 2013, pp. 1-5.
  21. H. Tang, S. I. Kamata, " A gradient based Predictive coding for lossless image coding, " in ICICE Transactions on Information and Systems, vol. E89, no. 7, pp. 2250-2256, July 2006.
  22. J. Oliver and M. P. Malumbres, "Low-Complexity Multiresolution Image Compression Using Wavelet Lower Trees," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 11, pp. 1437-1444, Nov. 2006.
  23. S. A. Martucci, "Reversible compression of HDTV images using median adaptive prediction and arithmetic coding," Circuits and Systems, 1990., IEEE International Symposium on, New Orleans, LA, 1990, pp. 1310-1313 vol.2.
  24. Xin Li and M. T. Orchard, "Edge-directed prediction for lossless compression of natural images," in IEEE Transactions on Image Processing, vol. 10, no. 6, pp. 813-817, Jun 2001.
  25. B. Meyer and P. E. Tischer, "Grey level image compression by adaptive weighted least squares," in Proceedings of Data Compression Conference 2001, Snowbird, Utah, USA, March 2001, pp. 503.
Index Terms

Computer Science
Information Sciences

Keywords

adaptive prediction bit rate compression ratio gradient estimation.

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