Adaptive reversible watermarking for authentication and privacy protection of medical records
- 75 Downloads
Medical systems, such as PACS or scanners, are vulnerable to security and forgery attacks. Consequently, medical records, such as patient information and medical imagery, can be easily leaked or forged. Reversible watermarking is an efficient solution used to protect medical records. However, previous studies have not sufficiently addressed medical applications. This study proposes an adaptive reversible watermarking algorithm that is directly applicable to medical systems that preserves the quality of medical imagery. In particular, the characteristics of medical imagery are considered. Once object and background regions are segmented, the reversible watermarking algorithm is applied based on an estimated error expansion approach. The watermark is embedded by expanding the estimated error from adjacent pixels. This watermark can include patient information or a hash code to detect forgery. When the watermark is extracted, original imagery is perfectly reconstructed without any quality degradation. Inherent over- and underflow problems are solved using an error pre-compensation technique. With the use of medical images from MRI, CT, and X-ray scanners, intensive experiments are performed to analyze the performance of the proposed algorithm with respect to capacity, perceptual quality, and reconstruction rate.
KeywordsMedical imagery Privacy protection Reversible watermarking Estimated error expansion Segmentation
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1 A1B03030432).
- 1.Abdeldaim AM, Sahlol AT, Elhoseny M, Hassanien AE (2018) Computer-aided acute lymphoblastic leukemia diagnosis system based on image analysis. In: Hassanien A, Oliva D (eds) Advances in soft computing and machine learning in image processing. Studies in computational intelligence, vol 730. Springer, pp 131–147. https://doi.org/10.1007/978-3-319-63754-9_7
- 8.Eltoukhy MM, Elhoseny M, Hosny KM, Singh AK (2018) Computer aided detection of mammographic mass using exact Gaussian-Hermite moments. J Ambient Intell Humaniz Comput:1–9. https://doi.org/10.1007/s12652-018-0905-1
- 13.Lee HY (2014) Reversible data hiding based on prediction-error expansion and error pre-compensation. Journal of Convergence Information Technology 8(16):48–62Google Scholar
- 27.Thakur S, Singh AK, Ghrera SP, Elhoseny M (2018) Multi-layer security of medical data through watermarking and chaotic encryption for tele-health applications. Multimed Tools Appl:1–14. https://doi.org/10.1007/s11042-018-6263-3
- 33.Yeo DG, Lee HY, Kim BM (2011) High capacity reversible watermarking using differential histogram shifting and predicted error compensation. J Electron Imaging 20(1). https://doi.org/10.1117/1.3532833