Prediction-Based Reversible Watermarking for Safe Transfer of CT Scan Medical Images

  • Nisar Ahmed MemonEmail author
  • Shakeel Ahmed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


Due to rapid development in the field of computer networks, exchange of medical records over the Internet has become common practice now-a-days. Medical records, which contain patient information and medical images, are exchanged among medical personnel for number of reasons. Discussing diagnostic and therapeutic measures is one of the main reasons of this exchange. Since patients are highly sensitive about their medical information; therefore these images require strict security. To achieve this stringent requirement digital image watermarking provides a good solution. In this paper, we present a prediction-based reversible medical image watermarking (MIW) system, which is comprised of two phases: embedding phase and extraction phase. In embedding phase, the proposed system, first divides the cover image in region of interest (ROI) and region of non-interest (RONI). Later it embeds a fragile watermark (FW) in ROI for achieving integrity control of ROI and a robust watermark (RW) in RONI for achieving security and authenticity of medical image. In extraction phase, the implanted watermarks are extracted first and then are compared with their reference watermarks to verify the integrity, security and confidentiality of the received image. We have used CT scan medical images for simulations. Experimental results show that proposed system provides better security and confidentiality for safe transfer of CT scan medical images. In addition, proposed system also outperforms the other reversible MIW techniques, which are currently reported in literature.


Prediction-based reversible watermarking Fragile watermarking Robust watermarking Authenticity Confidentiality LSB technique 



The authors are very grateful to College of Computer Science and Information Technology (CCSIT), King Faisal University, Saudi Arabia for providing the resources to carry out this research.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Computer Science and Information Technology (CCSIT)King Faisal UniversityAl-AhsaKingdom of Saudi Arabia

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