Journal of Digital Imaging

, Volume 29, Issue 2, pp 216–225 | Cite as

Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

  • Gran Badshah
  • Siau-Chuin Liew
  • Jasni Mohd Zain
  • Mushtaq Ali
Article

Abstract

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

Keywords

Teleradiology Ultrasound medical image Compression Digital watermark LZW lossless compression Secret key 

References

  1. 1.
    Nyeem H, Boles W, Boyd C: A review of medical image watermarking requirements for teleradiology. J Digit Imaging 26:326–343, 2012CrossRefPubMedCentralGoogle Scholar
  2. 2.
    Wenbo D, Chueh LP, Guan YL: An improved tamper detection and localization scheme for volumetric DICOM images. J Digit Imaging 25:751–763, 2012CrossRefGoogle Scholar
  3. 3.
    Zhang X, Wang S: Statistical fragile watermarking capable of locating individual tampered pixels. IEEE Signal Process 14:727–730, 2007CrossRefGoogle Scholar
  4. 4.
    Pujar JH, Kadlaskar LM: A new lossless method of image compression and decompression using Huffman coding technique. J Theoret Appl Inform Technol 15:18–22, 2010Google Scholar
  5. 5.
    Lim SJ, Moon H-M, Chae S-H, Chung Y and Pan SB: JPEG 2000 and digital watermarking technique using in medical image. IEEE 3rd International Conference on Secure Software Integration Reliability Improvement, SSIRI 2009, 413–416,2009Google Scholar
  6. 6.
    Liew SC and Zain JM: Reversible medical image watermarking for tamper detection and recovery. IEEE 3rd International Conference on Computer Science and Information Technology, (ICCSIT) 2010, 417–420,2010Google Scholar
  7. 7.
    Zhang Y, Wang S: Fragile watermarking with error-free restoration capability. IEEE Trans Multimed 10:1490–1499, 2008CrossRefGoogle Scholar
  8. 8.
    Gerhard C, Langelaar, Setyawan I and Reginald LL: Watermarking digital image and video data, a state-of-the-art overview. IEEE Sig Proce Magazine: 20–46,2000Google Scholar
  9. 9.
    Ferdinando DM, Salvatore S: Fragile watermarking tamper detection with images compressed by fuzzy transform. ELSEVIER 195:62–90, 2012Google Scholar
  10. 10.
    Das S, Kundu MK: Effective management of medical information through ROI-lossless fragile image watermarking technique. Comput Methods Prog Biomed 111:662–675, 2013CrossRefGoogle Scholar
  11. 11.
    Karadimitriou K, John MT: Min-max compression methods for medical image databases. ACM SIGMOD, 1997. doi: 10.1145/248603.248613 Google Scholar
  12. 12.
    Wong S, Zaremba L, Gooden D, Huang HK: Radiologic image compression—a review. Proc IEEE 83:194–218, 1995CrossRefGoogle Scholar
  13. 13.
    Gaidhane VH, Hote YV, Singh V: A new approach for estimation of eigenvalues of images. Int J Comput Applic 26:1–6, 2011CrossRefGoogle Scholar
  14. 14.
    Miaou SG, Lin C: A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals. IEEE Trans Biomed Eng 49:233–239, 2002CrossRefPubMedGoogle Scholar
  15. 15.
    Watanabe E and Mori K: Lossy image compression using a molecular structured neural network. Proceedings of IEEE Signal Processing Society Workshop: 403–412,2001Google Scholar
  16. 16.
    Hashwmi BS, Mahloojifar A, Akhvan A: Threshold Based Lossy Compression of Medical Ultrasound Images Using Contourlet Transform. IEEE Iranian Bio Medical Engineering conference, Tehran, 2011Google Scholar
  17. 17.
    Steven WS: Digital Signal Processing: The Scientist and Engineer’s Guide, 2nd edition. Technical Publishing San Diego, California, 1999Google Scholar
  18. 18.
    Vilas HG, Vijander, Yogesh VH, Mahendra K: New approaches for image compression using neural network. J Intell Learn Syst Applic 3:220–229, 2011CrossRefGoogle Scholar
  19. 19.
    Ma L, Khorasani K: Application of adaptive constructive neural network to image compression. IEEE Trans Neural Net 13:1112–1126, 2002CrossRefGoogle Scholar
  20. 20.
    Daszykowski M, Walczak B and Massart DL: A journey into low-dimensional spaces with autoassociative neural networks. DOI:  10.1016/S0039-9140(03)00018-3, 2003
  21. 21.
    Vilovic I: An experience in image compression using neural networks. Proceeding of 48th International Symposium Elmar: 95–98,2006Google Scholar
  22. 22.
    Gaidhane V, Singh V and Kumar M: Image compression using PCA and improved technique with MLP neural network. Proc IEEE Int Conf Adv Rec Technol Commun Comput, Kottayam: 106–110,2010Google Scholar
  23. 23.
    Daszykowski M, Walczak B, Massart DL: A journey into low-dimensional spaces with autoassociative neural networks. Talanta 59:1095–1105, 2003CrossRefPubMedGoogle Scholar
  24. 24.
    Talu MF, Turkoglu I: Hybrid lossless compression method for binary images. IU-JEEE 11:1399–1405, 2011Google Scholar
  25. 25.
    Franti P: A fast and efficient compression method for binary image. Signal Process Image Commun 6:69–76, 1994CrossRefGoogle Scholar
  26. 26.
    Burrows M, Wheeler DJ: A block-sorting lossless data compression algorithm. Syst Res Center 22:1–18, 1994Google Scholar
  27. 27.
    Meyer B and Tischer P: TMW—A new method for lossless image compression. Department of Computer Science Monash University Australia: DOI: 10.1.1.116.3891,1997Google Scholar
  28. 28.
    Frank YS, Wu YT: Robust watermarking and compression for medical images based on genetic algorithms. Elsevier Inform Sci 175:200–216, 2005CrossRefGoogle Scholar
  29. 29.
    Brittain NJ, El-Sakka MR: Grayscale true two dimensional dictionary-based image compression. J Vis Commun Image Represent 18:35–44, 2007CrossRefGoogle Scholar
  30. 30.
    Chen RC, Pai PY, Chan YK, Chang CC: Lossless image compression based on multiple-tables arithmetic coding. Math Probl Eng 2009:1–15, 2009Google Scholar
  31. 31.
    Zhou L: A new highly efficient algorithm for lossless binary image compression. Dissertation on image compression: Prince George University of North British Columbia,2004Google Scholar
  32. 32.
    Liew S-C, Liew S-W, Zain JM: Tamper localization and lossless recovery watermarking scheme with ROI segmentation and multilevel authentication. J Digit Imaging 26:316–325, 2013CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Badshah G, Liew SC, Zain JM, Hisham SI, Zehra A: Importance of watermark lossless compression in digital medical image watermarking. Res J Rec Sci 4:75–79, 2015Google Scholar
  34. 34.
    Burg J: The science of digital media, digital image processing. Gran No. 0340969: 49–51,2007Google Scholar
  35. 35.
    Burg J and Lausier A: Supplement to chapter 3 of the science of digital media- digital image processing. The National Science Foundation supported research, Grant Numbers. DUE-0127280, DUE-0340969: 1–13,2007Google Scholar
  36. 36.
    Nelson M, Hakenberg J, Littlewood D and Snyder J: LZW Data compression. Dr. Dobb’s Journal: 1989Google Scholar
  37. 37.
    Alarabeyyat J, Al-Hashemi S, Khdour T, Btoush MH, Bani-Ahmad S, Al-Hashemi: Lossless image compression technique using combination methods. J Softw Eng Appl 5:752–763, 2012CrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2015

Authors and Affiliations

  • Gran Badshah
    • 1
  • Siau-Chuin Liew
    • 1
  • Jasni Mohd Zain
    • 1
  • Mushtaq Ali
    • 1
  1. 1.Faculty of Computer Systems and Software EngineeringUniversiti Malaysia Pahang (UMP)Gambang KuantanMalaysia

Personalised recommendations