A New Approach for Image Compression Using Efficient Coding Technique and BPN for Medical Images

  • M. Rajasekhar ReddyEmail author
  • M. Akkshya Deepika
  • D. Anusha
  • J. Iswariya
  • K. S. Ravichandran
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Medical images produce a digital form of human body pictures. Most of the medical images contain large volumes of image data that is not used for further analysis. There exists a need to compress these images for storage issues and to produce a high-quality image. This paper discusses an image compression using Back Propagation Neural network (BPN) and an efficient coding technique for MRI images. Image compression using BPN produces an image without degrading its quality and it requires less encoding time. An efficient coding technique—Arithmetic coding is used to produce an image with better compression ratio and redundancy is much reduced. Back-Propagation Neural Network with arithmetic coding gives the better results.


Arithmetic coding Image compression BPN PSNR MSE 


  1. 1.
    Dougherty G (2009) Digital image processing for medical applications, 1st edn. Cambridge University Press, Cambridge, pp 9–10CrossRefGoogle Scholar
  2. 2.
    Shapiro JM (1993) Embedded image coding using zero trees of wavelet coefficients. IEEE Trans Sig Process 41(12):3445–3462CrossRefGoogle Scholar
  3. 3.
    Maha Lakshmi GV, Rama Mohana Rao S (2012) A novel algorithm for image compression based on fractal. Image Compression EJSR 85(4):486–499. ISSN: 1450–216X, EurojournalsGoogle Scholar
  4. 4.
    Savkovic-Stevanovic J (1994) Neural networks for process analysis and optimization: modeling and application. Comput Chem Eng 18(11–12):1149–1155 (14 Ref.)CrossRefGoogle Scholar
  5. 5.
    Soliman HS, Omari M (2006) A neural networks approach to image data compression. Appl Soft Comput 6(3):258–271CrossRefGoogle Scholar
  6. 6.
    Maha Lakshmi GV, Rama Mohana Rao S (2013) A novel algorithm for image compression based on fractal and neural networks. Int J Eng Innov Technol (IJEIT) 3(4):8–15Google Scholar
  7. 7.
    Kavitha V, Easwarakumar KS (2008) Enhancing privacy in arithmetic coding. ICGST-AIML J 8(I):23–28Google Scholar
  8. 8.
    Masmoudi A, Masmoudi A (2013) A new arithmetic coding model for a block-based lossless image compression based on exploiting inter-block correlation. Sig Image Video Process 1–7Google Scholar
  9. 9.
    Witten IH, Neal R, Gleary JG (1987) Arithmetic coding for data compression. Commun ACM 30(4):520–540CrossRefGoogle Scholar
  10. 10.
    Vilovic I (2006) An experience in image compression using neural networks. In: Proceedings of the 48th international symposium ELMAR-2006 focused on multimedia signal processing and communications, June 2006. IEEE Press, Zadar, Croatia, pp 95–98Google Scholar
  11. 11.
    Meyer-Bäse A, Jancke K, Wismüller A, Foo S, Martinetz T (2005) Medical image compression using topology-preserving neural networks. Eng Appl Artif Intell 18(4):383–392CrossRefGoogle Scholar
  12. 12.
    Carrato S (1992) Neural networks for image compression. In: Neural networks: advancement and application, 2 edn. Galenbe Pub, North-Holland, Amsterdam, pp 177–198CrossRefGoogle Scholar
  13. 13.
    Srikala P, Umar S (2012) Neural network based image compression with lifting scheme and RLC. Int J Res Eng Technol 1(1):13–19CrossRefGoogle Scholar
  14. 14.
    Osowski S, Waszczuk R, Bojarczak P (2006) Image compression using feed forward neural networks—hierarchical approach. In: Natural to artificial neural computation, vol 3497 of Lecture notes in computer science. Springer, Berlin, Germany, pp 1009–1015Google Scholar
  15. 15.
    Ashraf R, Akbar M (2007) Adaptive architecture neural nets for medical image compression. In: Proceedings of the IEEE international conference on engineering of intelligent systems (ICEIS ’06), Islamabad, Pakistan, pp 1–4Google Scholar
  16. 16.
    Northan B, Dony RD (2006) Image compression with a multiresolution neural network. Can J Electr Comput Eng 31(1):49–58CrossRefGoogle Scholar
  17. 17.
    ISO/IEC 14496-2 FPDAM 1 Information Technology. Generic Coding of Audio-Visual Objects—Part 2: Visual, July 1999Google Scholar
  18. 18.
    Rubin F (1979) Arithmetic stream coding using fixed precision registers. IEEE Trans Inf Theory 25:672–675 (Data compression conference)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Miaou S-G, Ke F-S, Chen S-C (2009) A lossless compression method for medical image sequences using JPEG-LS and interframe coding. IEEE Trans Inf Technol Biomed 13(5):818–821CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Rajasekhar Reddy
    • 1
    Email author
  • M. Akkshya Deepika
    • 1
  • D. Anusha
    • 1
  • J. Iswariya
    • 1
  • K. S. Ravichandran
    • 1
  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia

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