Advertisement

Medical Image Spatial Fusion Watermarking System

  • P. Viswanathan
  • P. VenkataKrishna
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

The watermarking based on wavelet fusion gains sharp edge and discontinuity in watermarked image due to embedding in smooth region. To solve this problem spatial fusion watermarking method is proposed in this paper. The image is decomposed into four levels by predicting the range of intensity. To improve the capacity of embedding, higher counted region is chosen for embedding which indirectly chooses sharp region. The Text or ROI data is watermarked in the chosen region by differentiating the pixel with one by the constraint of even or odd between the cover image and text or ROI. The decomposed images are composed by averaging the pixels of the regions greater than one. After extraction, the watermarked medical image is reconstructed to original medical image by reversible property. This system is evaluated with various metrics using standard medical images which show good quality and high imperceptibility with embedding capacity.

Keywords

Fusion Watermarking Decomposition Extraction Composition and reversible 

References

  1. 1.
    Viswanathan P, Venkata Krishna P (2009) Text fusion watermarking in medical image with semi-reversible for secure transfer and authentication. In: International conference on advances in recent technologies in communication and computing. ARTCom ‘09, pp 585–589, 27–28 Oct 2009Google Scholar
  2. 2.
    Viswanathan P, VenkataKrishna P (2011) Fusion of cryptographic watermarking medical image system with reversible property. ICTACT Int J Image Video Process 2(1):258–263Google Scholar
  3. 3.
    Gutierrez CN, Kakani G, Verma RC, Wang T (2010) Digital watermarking of medical images for mobile devices. In: IEEE international conference on sensor networks, ubiquitous, and trustworthy computing (SUTC), pp 421–425, 7–9 June 2010Google Scholar
  4. 4.
    Tu ZW, Chen XR, Yuille AL, Zhu SC (2005) Image parsing: unifying segmentation, detection, and recognition. Int J Comput Vis 63(2):113–140CrossRefGoogle Scholar
  5. 5.
    Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540CrossRefGoogle Scholar
  6. 6.
    Pajares G, de la Cruz JM (2004) A wavelet-based image fusion tutorial. Pattern Recog 37(9):1855–1872CrossRefGoogle Scholar
  7. 7.
    Ray LA, Adhami RR (2006) Dual tree discrete wavelet transform with application to image fusion. In: Southeastern symposium on system theory, pp 430–433Google Scholar
  8. 8.
    Xydeas CS, Petrovic V (2004) Gradient-based multiresolution image fusion. IEEE Trans Image Process 13(2):228–237CrossRefGoogle Scholar
  9. 9.
    Toet A (1989) Image fusion by a ratio of low-pass pyramid. Pattern Recogn Lett 9:245–253MATHCrossRefGoogle Scholar
  10. 10.
    Toet A, van Ruyven LJ, Valeton JM (1989) Merging thermal and visual images by a contrast pyramid. Opt Eng 28(7):789–792CrossRefGoogle Scholar
  11. 11.
    Simoncelli EP, Freeman WT, Adelson EH, Heeger DJ (1992) Shiftable multi-scale transforms. IEEE Trans Inf Theor 38(2):587–607MathSciNetCrossRefGoogle Scholar
  12. 12.
    Qu GH, Zhang DL, Yan PF (2002) Information measure for performance of image fusion. IEEE Electron Lett 38(7):313–315CrossRefGoogle Scholar
  13. 13.
    Lifeng Y, Donglin Z, Weidong W, Shanglian B (2001) Multi-modality medical image fusion based on wavelet pyramid and evaluation. Syst Eng Electron 12:42–48Google Scholar
  14. 14.
    Wang Q, Shen Y (2006) Performance assessment of image fusion. In: Advances in image and video technology, Springer, Berlin, pp 373–382Google Scholar
  15. 15.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  16. 16.
    Wang Q, Shen Y (2006) Performance assessment of image fusion. In: Advances in image and video technology, Springer, Berlin, pp 373–382Google Scholar
  17. 17.
    Xydeas CS, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309Google Scholar
  18. 18.
    DICOM image data download http://barre.nom.fr/medical/samples/
  19. 19.
    IEEE visualization context data download http://viscontest.sdsc.edu/2010/data.html
  20. 20.
    Ives JR, Warach S, Schmitt F, Edelman RR, Schomer DL (1993) Monitoring the patient’s EEG during echo-planar MRI. Electroenceph Clin Neurophysiol 87:417–420Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Infromation TechnologyVIT UniversityVelloreIndia
  2. 2.Computer Science EngineeringVIT UniversityVelloreIndia

Personalised recommendations