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Neural Computing and Applications

, Volume 30, Issue 7, pp 2017–2028 | Cite as

A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram computation and quartered interpolation with greedy weights

  • Mohammad Reza KhosraviEmail author
  • Mehran Yazdi
S.I. : Deep Learning for Biomedical and Healthcare Applications
First Online:

Abstract

In the digital world, watermarking technology is a solution for data hiding and completely essential for management and secure communications of digital data propagated over the internet-based platforms. Reversible watermarking is a quality-aware type of watermarking which has been applied in managing digital contents such as digital images, texts, audios and videos. Reversible watermarking is also known as lossless watermarking due to its preservation of all details of host and hidden data. One of the important uses of this kind of watermarking is to manage medical data regarding DICOM images. In the recent years, a new type of reversible watermarking technology entitled interpolation-based reversible watermarking has been introduced, and we are going to enhance it for DICOM images by using a hybrid approach based on computing error histogram and by applying an image interpolation with greedy weights (adaptive weighting). In practice, simulation results clearly show better performance of the proposed scheme compared to the previous techniques using interpolation-based reversible watermarking on different DICOM images.

Keywords

Reversible watermarking Digital Imaging and Communications in Medicine (DICOM) Interpolation-Based Reversible Watermarking (IBRW) Error histogram Greedy Weights in Quartered Interpolation (GWQI) Differential Expansion (DE) 
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Notes

Acknowledgements

The authors warmly thank Mohammad Arabzadeh for his support. In addition, we would like to thank all reviewers and editors for their helpful comments and efforts.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Abd-Eldayem M (2013) A proposed security technique based on watermarking and encryption for Digital Imaging and Communications in Medicine. Egypt Inform J 14:1–13CrossRefGoogle Scholar
  2. 2.
    Lin C-C, Tai W-L, Chang C-C (2008) Multilevel reversible data hiding based on histogram modification of difference images. Pattern Recogn 41:3582–3591CrossRefGoogle Scholar
  3. 3.
    Luo L, Chen Z, Chen M, Zeng X, Xiong Z (2010) Reversible image watermarking using interpolation technique. IEEE Trans Inf Forensics Secur 5(1):187–193CrossRefGoogle Scholar
  4. 4.
    Arabzadeh M, Helfroush MS, Danyali H, Rahimi MR (2011) DE-based reversible medical image authentication using hamming code. In: International e-conference on computer and knowledge engineering (ICCKE), pp 183–188Google Scholar
  5. 5.
    Arabzadeh M, Danyali H, Helfroush MS (2010) Reversible watermarking based on interpolation error histogram shifting. In: International symposium on telecommunications (IST’2010), pp 840–845Google Scholar
  6. 6.
    Dragoi I, Coltuc D (2014) Local-prediction-based difference expansion reversible watermarking. IEEE Trans Image Process 23(4):1779–1790MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wen J, Lei J, Wan Y (2012) Reversible data hiding through adaptive prediction and prediction error histogram modification. Int J Fuzzy Syst 14(2):244–256Google Scholar
  8. 8.
    Zhang S, Gao T, Yang L (2016) A reversible data hiding scheme based on histogram modification in integer DWT domain for BTC compressed images. Int J Netw Secur 18(4):718–727Google Scholar
  9. 9.
    Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circuits Syst Video Technol 13(8):890–896CrossRefGoogle Scholar
  10. 10.
    Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, NJGoogle Scholar
  11. 11.
    Zhang L, Wu X (2005) Color demosaicking via directional linear minimum mean square-error estimation. IEEE Trans Image Process 14(12):2167–2178CrossRefGoogle Scholar
  12. 12.
    Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238CrossRefGoogle Scholar
  13. 13.
    Zhang L, Wu X, Buades A, Li X (2011) Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J Electron Imaging 20(2):023016CrossRefGoogle Scholar
  14. 14.
    Getreuer P (2011) Zhang-Wu directional LMMSE image demosaicking. Image Process Line (IPOL) 1:117–126Google Scholar
  15. 15.
    Lee S, Kang M, Uhm K, Ko S (2016) An edge-guided image interpolation method using taylor series approximation. IEEE Trans Consum Electron 62(2):159–165CrossRefGoogle Scholar
  16. 16.
    Baghaie A, Yu Z (2015) Structure tensor based image interpolation method. Int J Electron Commun (AEÜ) 69:515–522CrossRefGoogle Scholar
  17. 17.
    Khosravi MR, Rostami H (2016) A new statistical technique for interpolation of landsat images. In: ICAUCAE 2016, SID Conference Publications, Tehran, IranGoogle Scholar
  18. 18.
    Colonnese S, Rinauro S, Scarano G (2013) Bayesian image interpolation using Markov random fields driven by visually relevant image features. Sig Process Image Commun 28:967–983CrossRefGoogle Scholar
  19. 19.
    Malik A, Sikka G, Verma H (2016) An image interpolation based reversible data hiding scheme using pixel value adjusting feature. Multimedia Tools Appl 76:13025–13046CrossRefGoogle Scholar
  20. 20.
    Chang Y-T, Huang C-T, Lee C-F, Wang S-J (2013) Image interpolating based data hiding in conjunction with pixel-shifting of histogram. J Supercomput 66:1093–1110CrossRefGoogle Scholar
  21. 21.
    Lu T-C, Chang C-C, Huang Y-H (2014) High capacity reversible hiding scheme based on interpolation, difference expansion, and histogram shifting. Multimedia Tools Appl 72:417–435CrossRefGoogle Scholar
  22. 22.
    Golpira H, Danyali H (2011) Reversible medical image watermarking based on wavelet histogram shifting. Imaging Sci J 59:49–59CrossRefGoogle Scholar
  23. 23.
    Lei B, Tan E, Chen S, Ni D, Wang T, Lei H (2014) Reversible watermarking scheme for medical image based on differential evolution. Expert Syst Appl 41:3178–3188CrossRefGoogle Scholar
  24. 24.
    Rocek A (2016) A new approach to fully-reversible watermarking in medical imaging with breakthrough visibility parameters. Biomed Signal Process Control 29:44–52CrossRefGoogle Scholar
  25. 25.
    Ni Z, Shi Y, Ansari N, Su W (2006) Reversible data hiding. IEEE Trans Circuits Syst Video Technol 16(3):354–362CrossRefGoogle Scholar
  26. 26.
    Hwang J, Kim JW, Choi JU (2006) A reversible watermarking based on histogram shifting. Lecture notes in computer science. Springer, BerlinCrossRefGoogle Scholar
  27. 27.
    Hu Y, Lee H, Li J (2009) DE-based reversible data hiding with improved overflow location map. IEEE Trans Circuits Syst Video Technol 19(2):250–260CrossRefGoogle Scholar
  28. 28.
    Arabzadeh M, Rahimi MR (2012) Reversible data hiding scheme based on maximum histogram gap of image blocks. KSII Trans Internet Inf Syst 6(8):1964–1981Google Scholar
  29. 29.
    Arabzadeh M, Helfroush MS, Danyali H, Kasiri K (2011) Reversible watermarking based on generalized histogram shifting. In: 18th IEEE international conference on image processing (ICIP), pp 2741–2744Google Scholar
  30. 30.
    Tan CK, Ng JC, Xu X, Poh CL, Guan YL, Sheah K (2011) Security protection of DICOM medical images using dual-layer reversible watermarking with tamper detection capability. J Digit Imaging 24(3):528–540CrossRefGoogle Scholar
  31. 31.
    Khalil MI (2017) Medical image steganography: study of medical image quality degradation when embedding data in the frequency domain. Int J Comput Netw Inf Secur 9:22Google Scholar
  32. 32.
    Kelkar V, Tuckley K, Nemade H (2017) Novel variants of a histogram shift-based reversible watermarking technique for medical images to improve hiding capacity. J Healthcare Eng.  https://doi.org/10.1155/2017/3538979 CrossRefGoogle Scholar
  33. 33.
    Manimehalai P, Rani PAJ (2016) A new robust reversible blind watermarking in wavelet-domain for color images. Int J Image Graph 16(2):1650006CrossRefGoogle Scholar
  34. 34.
    Khosravi MR, Sharif-Yazd M, Moghimi MK, Keshavarz A, Rostami H, Mansouri S (2017) MRF-based multispectral image fusion using an adaptive approach based on edge-guided interpolation. J Geogr Inf Syst 9(2):114–125Google Scholar
  35. 35.
  36. 36.
  37. 37.
  38. 38.
    Alhihi M (2017) Determining the optimum number of paths for realization of multi-path routing in MPLS-TE networks. TELKOMNIKA 15(4):1701–1709CrossRefGoogle Scholar
  39. 39.
    Khosravi MR, Basri H, Rostami H (2018) Efficient routing for dense UWSNs with high-speed mobile nodes using spherical divisions. J Supercomput 74(2):696–716CrossRefGoogle Scholar
  40. 40.
    Wen W, Zhang Y, Fang Y, Fang Z (2018) Image salient regions encryption for generating visually meaningful ciphertext image. Neural Comput Appl 29(3):653–663CrossRefGoogle Scholar
  41. 41.
    Khan M, Asghar Z (2018) A novel construction of substitution box for image encryption applications with Gingerbreadman chaotic map and S8 permutation. Neural Comput Appl 29(4):993–999CrossRefGoogle Scholar
  42. 42.
    Khosravi MR, Rostami H, Samadi S (2018) Enhancing the binary watermark-based data hiding scheme using an interpolation-based approach for optical remote sensing images. Int J Agric Environ Inf Syst 9(2):53–71CrossRefGoogle Scholar
  43. 43.
    Kala R, Deepa P (2017) Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images. Neural Comput Appl.  https://doi.org/10.1007/s00521-017-2953-4 CrossRefGoogle Scholar
  44. 44.
    Dianat R, Ghanbari M (2015) Comparison between various standard definition to high definition image conversion methods. Recent Adv Commun Netw Technol 4:6–15CrossRefGoogle Scholar
  45. 45.
    Bazargani M, Ebrahimi H, Dianat R (2012) Digital image watermarking in wavelet, contourlet and curvelet domains. J Basic Appl Sci Res 11(2):11296–11308Google Scholar
  46. 46.
    Li CH, Lu ZM, Su YX (2011) Reversible data hiding for BTC-compressed images based on bit-plane flipping and histogram shifting of mean tables. Inf Technol J 10(7):1421–1426CrossRefGoogle Scholar
  47. 47.
    Lo CC, Hu YC, Chen WL, Wu CM (2014) Reversible data hiding scheme for BTC-compressed images based on histogram shifting. Int J Secur Appl 8(2):301–314Google Scholar
  48. 48.
    Ou B, Li X, Zhao Y, Ni R, Shi YQ (2013) Pairwise prediction-error expansion for efficient reversible data hiding. IEEE Trans Image Process 22(12):5010–5021MathSciNetCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran
  2. 2.Computer Engineering DepartmentPersian Gulf UniversityBushehrIran
  3. 3.Department of Communications and Electronic EngineeringShiraz UniversityShirazIran

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