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

  • S.I. : Deep Learning for Biomedical and Healthcare Applications
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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.

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References

  1. Abd-Eldayem M (2013) A proposed security technique based on watermarking and encryption for Digital Imaging and Communications in Medicine. Egypt Inform J 14:1–13

    Article  Google Scholar 

  2. Lin C-C, Tai W-L, Chang C-C (2008) Multilevel reversible data hiding based on histogram modification of difference images. Pattern Recogn 41:3582–3591

    Article  Google Scholar 

  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):187–193

    Article  Google Scholar 

  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 183–188

  5. Arabzadeh M, Danyali H, Helfroush MS (2010) Reversible watermarking based on interpolation error histogram shifting. In: International symposium on telecommunications (IST’2010), pp 840–845

  6. Dragoi I, Coltuc D (2014) Local-prediction-based difference expansion reversible watermarking. IEEE Trans Image Process 23(4):1779–1790

    Article  MathSciNet  Google Scholar 

  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):244–256

    Google Scholar 

  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):718–727

    Google Scholar 

  9. Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circuits Syst Video Technol 13(8):890–896

    Article  Google Scholar 

  10. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, NJ

    Google Scholar 

  11. Zhang L, Wu X (2005) Color demosaicking via directional linear minimum mean square-error estimation. IEEE Trans Image Process 14(12):2167–2178

    Article  Google Scholar 

  12. Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238

    Article  Google Scholar 

  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):023016

    Article  Google Scholar 

  14. Getreuer P (2011) Zhang-Wu directional LMMSE image demosaicking. Image Process Line (IPOL) 1:117–126

    Google Scholar 

  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):159–165

    Article  Google Scholar 

  16. Baghaie A, Yu Z (2015) Structure tensor based image interpolation method. Int J Electron Commun (AEÜ) 69:515–522

    Article  Google Scholar 

  17. Khosravi MR, Rostami H (2016) A new statistical technique for interpolation of landsat images. In: ICAUCAE 2016, SID Conference Publications, Tehran, Iran

  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 28:967–983

    Article  Google Scholar 

  19. Malik A, Sikka G, Verma H (2016) An image interpolation based reversible data hiding scheme using pixel value adjusting feature. Multimedia Tools Appl 76:13025–13046

    Article  Google Scholar 

  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 66:1093–1110

    Article  Google Scholar 

  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 72:417–435

    Article  Google Scholar 

  22. Golpira H, Danyali H (2011) Reversible medical image watermarking based on wavelet histogram shifting. Imaging Sci J 59:49–59

    Article  Google Scholar 

  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 41:3178–3188

    Article  Google Scholar 

  24. Rocek A (2016) A new approach to fully-reversible watermarking in medical imaging with breakthrough visibility parameters. Biomed Signal Process Control 29:44–52

    Article  Google Scholar 

  25. Ni Z, Shi Y, Ansari N, Su W (2006) Reversible data hiding. IEEE Trans Circuits Syst Video Technol 16(3):354–362

    Article  Google Scholar 

  26. Hwang J, Kim JW, Choi JU (2006) A reversible watermarking based on histogram shifting. Lecture notes in computer science. Springer, Berlin

    Book  Google Scholar 

  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):250–260

    Article  Google Scholar 

  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):1964–1981

    Google Scholar 

  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 2741–2744

  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):528–540

    Article  Google Scholar 

  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 9:22

    Google Scholar 

  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

    Article  Google Scholar 

  33. Manimehalai P, Rani PAJ (2016) A new robust reversible blind watermarking in wavelet-domain for color images. Int J Image Graph 16(2):1650006

    Article  Google Scholar 

  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):114–125

    Google Scholar 

  35. http://www.dicomlibrary.com

  36. http://www.aycan.de/sample-dicom-images.html

  37. http://dicom.nema.org

  38. Alhihi M (2017) Determining the optimum number of paths for realization of multi-path routing in MPLS-TE networks. TELKOMNIKA 15(4):1701–1709

    Article  Google Scholar 

  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):696–716

    Article  Google Scholar 

  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):653–663

    Article  Google Scholar 

  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):993–999

    Article  Google Scholar 

  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):53–71

    Article  Google Scholar 

  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

    Article  Google Scholar 

  44. Dianat R, Ghanbari M (2015) Comparison between various standard definition to high definition image conversion methods. Recent Adv Commun Netw Technol 4:6–15

    Article  Google Scholar 

  45. Bazargani M, Ebrahimi H, Dianat R (2012) Digital image watermarking in wavelet, contourlet and curvelet domains. J Basic Appl Sci Res 11(2):11296–11308

    Google Scholar 

  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):1421–1426

    Article  Google Scholar 

  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):301–314

    Google Scholar 

  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):5010–5021

    Article  MathSciNet  Google Scholar 

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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.

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Correspondence to Mohammad Reza Khosravi.

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Khosravi, M.R., Yazdi, M. A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram computation and quartered interpolation with greedy weights. Neural Comput & Applic 30, 2017–2028 (2018). https://doi.org/10.1007/s00521-018-3489-y

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  • DOI: https://doi.org/10.1007/s00521-018-3489-y

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