Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 21, pp 28685–28708 | Cite as

Distinguishable zero-watermarking scheme with similarity-based retrieval for digital rights Management of Fundus Image

  • Beiji Zou
  • Jingyu Du
  • Xiyao LiuEmail author
  • Yifan Wang
Article
  • 128 Downloads

Abstract

Zero-watermarking scheme can provide durable and distortion-free digital rights management (DRM) for fundus image which plays an important role in diagnosis of ocular diseases. However, existing zero-watermarking schemes probably identify a similar fundus image as a copy, because they rarely consider the distinguishability for image. In addition, when the number of fundus images is large, it is difficult to obtain corresponding ownership shares accurately for copyright identification, because there is no retrieval mechanism in these schemes. To address these issues, a distinguishable zero-watermarking scheme which fuses similarity-based retrieval is proposed for DRM of fundus image. In our proposed scheme, distinguishable and robust features of fundus images are extracted based on the gray-scale variation. The ownership shares are constructed using visual secret sharing (VSS) by combining watermark and the master shares generated from these features. Once a suspected fundus image is found, the similarity-based retrieval is performed to retrieve the corresponding ownership share based on the feature of suspected image. After that, the copyright is identified by stacking the master share of suspected image and the retrieved ownership share. Experimental results on three public databases demonstrate that 1) Ownership shares corresponding to specific fundus images can be retrieved precisely. When fixing the false positive rate to 0.001, the mean false negative rates are not higher than 0.0693. 2) Copyrights of fundus images can be identified accurately and reliably. The mean bit error rates of recovered watermark are not higher than 0.0460.

Keywords

Digital rights management Fundus image Gray-scale variation Similarity-based retrieval Zero-watermarking 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundations of China (61573380, 61602527), Natural Science Foundation of Hunan Province (2017JJ3416) and China Postdoctoral Science Foundation (2017M612585).

References

  1. 1.
    An L, Gao X, Yuan Y, Tao D, Deng C, Ji F (2012) Content-adaptive reliable robust lossless data embedding. Neurocomputing 79:1–11CrossRefGoogle Scholar
  2. 2.
    An L, Gao X, Li X, Tao D, Deng C, Li J (2012) Robust reversible watermarking via clustering and enhanced pixel-wise masking. IEEE T Image Process 21(8):3598–36112MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481CrossRefGoogle Scholar
  4. 4.
    Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed image database: the MESSIDOR database. Image Analysis and Stereology 33(3):231–234CrossRefGoogle Scholar
  5. 5.
    Deng X, Mao Y, Hu J (2012) A novel lossless robust medical image watermarking algorithm based on huffman coding and k-means clustering. JDCTA 6(13):368–377CrossRefGoogle Scholar
  6. 6.
    Dong P, Brankov JG, Galatsanos NP et al (2005) Digital watermarking robust to geometric distortions. IEEE T Image Process 14(12):2140–2150CrossRefGoogle Scholar
  7. 7.
    Dong C, Zhang H, Li J, Chen YW (2012) Robust zero-watermarking for medical image based on DCT. In: Int. Conf. Computer Sciences and Convergence Information Technology (ICCIT), pp 900–904Google Scholar
  8. 8.
    Esmaeili MM, Fatourechi M, Ward RK (2011) A robust and fast video copy detection system using content-based fingerprinting. IEEE T Inf Foren Sec 6(1):213–226CrossRefGoogle Scholar
  9. 9.
    Gao G, Jiang G (2013) A lossless copyright authentication scheme based on Bessel-Fourier moment and extreme learning machine in curvature-feature domain. J Syst Softw 86(1):222–232CrossRefGoogle Scholar
  10. 10.
    Gao G, Jiang G (2015) Bessel-Fourier moment-based robust image zero-watermarking. Multimed Tools Appl 74(3):841–858CrossRefGoogle Scholar
  11. 11.
    Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16(1):216–226CrossRefGoogle Scholar
  12. 12.
    Gunjal BL, Mali SN (2012) ROI based embedded watermarking of medical images for secured communication in telemedicine. Int J Comput Commun Eng 6(48):293–298Google Scholar
  13. 13.
    Han B, Cai L, Li W (2015) Zero-watermarking algorithm for medical volume data based on legendre chaotic neural network and perceptual hashing. IJGDC 8(1):201–212CrossRefGoogle Scholar
  14. 14.
    Lee HK, Kim H J, Kwon KR, Lee JK (2005) ROI medical image watermarking using DWT and bit-plane. In Asia-Pacific Conf Communications, pp 512–515Google Scholar
  15. 15.
    Lei B, Tan EL, Chen S, Ni D, Wang T, Lei H (2014) Reversible watermarking scheme for medical image based on differential evolution. Expert Syst Appl 41(3):3178–3188CrossRefGoogle Scholar
  16. 16.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In: International Conference on Artif Intell, pp 1617–1623Google Scholar
  17. 17.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. In: International Conference on Artif IntellGoogle Scholar
  18. 18.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  19. 19.
    Liu X, Li F, Du J et al (2017) A robust and synthesized-unseen watermarking for the DRM of DIBR-based 3D video. Neurocomputing 222:155–169CrossRefGoogle Scholar
  20. 20.
    Mao J, Xiao G, Sheng W, Hu Y, Qu Z (2016) A method for video authenticity based on the fingerprint of scene frame. Neurocomputing 173(3):2022–2032CrossRefGoogle Scholar
  21. 21.
    Memon NA, Keerio ZA, Abbasi F (2013) Dual watermarking of CT scan medical images for content authentication and copyright protection. In Int. Conf. Multi topic (IMTIC), pp 173–183Google Scholar
  22. 22.
    Naor M, Shamir A (1995) Visual cryptography. In: Advances in cryptology, pp 1–12Google Scholar
  23. 23.
    Pandey R, Singh AK, Kumar B, Mohan A (2016) A Iris based secure NROI multiple eye image watermarking for teleophthalmology. Multimed Tools Appl 75(22):14381–14397CrossRefGoogle Scholar
  24. 24.
    Parah SA, Sheikh JA, Ahad F, Loan AN, Bhat GM (2017) Information hiding in medical images: a robust medical image watermarking system for E-healthcare. Multimed Tools Appl 76(8):10599–10633CrossRefGoogle Scholar
  25. 25.
    Priyanka SM (2016) Region-based hybrid medical image watermarking for secure telemedicine applications. Multimed Tools Appl 76(3):3617–3647CrossRefGoogle Scholar
  26. 26.
    Rawat S, Raman B (2012) A blind watermarking algorithm based on fractional Fourier transform and visual cryptography. Signal Process 92(6):1480–1491CrossRefGoogle Scholar
  27. 27.
    Sarkar A, Singh V, Ghosh P, Manjunath BS, Singh A (2010) Efficient and robust detection of duplicate videos in a large database. IEEE T Circ Syst Vid 20(6):870–885CrossRefGoogle Scholar
  28. 28.
    Seenivasagam V, Velumani R (2013) A QR code based zero-watermarking scheme for authentication of medical images in teleradiology cloud. Comput Math Method M.  https://doi.org/10.1155/2013/516465 MathSciNetCrossRefGoogle Scholar
  29. 29.
    Singh TR, Singh KM, Roy S (2013) Video watermarking scheme based on visual cryptography and scene change detection. AEU-Int J Electron Commun 67(8):645–651CrossRefGoogle Scholar
  30. 30.
    Sivaswamy J, Krishnadas SR, Chakravarty A, Joshi GD, Tabish AS (2015) A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedical Imaging Data Papers 2(1):1004Google Scholar
  31. 31.
    Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE T Med Imaging 23(4):501–509CrossRefGoogle Scholar
  32. 32.
    Vellaisamy S, Ramesh V (2014) Inversion attack resilient zero-watermarking scheme for medical image authentication. IET Image Process 8(12):718–727CrossRefGoogle Scholar
  33. 33.
    Wolfgang RB, Delp EJ (1996) A watermark for digital images. In Proc. Int. Conf. Image process (ICIP), pp 219–222Google Scholar
  34. 34.
    Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE T Med Imaging 21(10):1236–1243CrossRefGoogle Scholar
  35. 35.
    Zhu C, Zou B, Xiang Y, Cui J, Wu H (2016) An ensemble retinal vessel segmentation based on supervised learning in fundus images. Chin J Electron 25(3):503–511CrossRefGoogle Scholar
  36. 36.
    Zhu C, Zou B, Zhao R, Cui J, Duan X, Chen Z, Liang Y (2017) Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput Med Imaging Graph 55:68–77CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Center for Ophthalmic Imaging ResearchCentral South UniversityChangshaChina

Personalised recommendations