Abstract: It is paramount to ensure the integrity and authenticity of medical images in telemedicine. This paper proposes an imperceptible and reversible Medical Image Watermarking (MIW) scheme based on image segmentation, image prediction and nonlinear difference expansion for integrity and authenticity of medical images and detection of both intentional and unintentional manipulations. The metadata from the Digital Imaging and Communications in Medicine (DICOM) file constitutes the authentication watermark while the integrity watermark is computed from Secure Hash Algorithm (SHA)-256. The two watermarks are combined and compressed using the Lempel Ziv (LZ) -77 algorithm. The scheme takes advantage of the large smooth areas prevalent in medical images. It predicts the smooth regions with zero error or values close to zero error, while non-smooth areas are predicted with large error values. The binary watermark is encoded and extracted in the zero-prediction error using a nonlinear difference expansion. The binary watermark is concentrated more on the Region of non-interest (RONI) than the Region of interest (ROI) to ensure a high visual quality while maintaining a high capacity. The paper also presents a separate low degradation side information processing algorithm to handle overflow. Experimental results show that the scheme is reversible and has a remarkable imperceptibility and capacity that are comparable to current works reported in literature.

David Muigai, Elijah Mwangi and Edwell T. Mharakurwa, “A Prediction Error Nonlinear Difference Expansion Reversible Watermarking for Integrity and Authenticity of DICOM Medical Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(3), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130326

@article{Muigai2022,
title = {A Prediction Error Nonlinear Difference Expansion Reversible Watermarking for Integrity and Authenticity of DICOM Medical Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130326},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130326},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {3},
author = {David Muigai and Elijah Mwangi and Edwell T. Mharakurwa}
}