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A novel zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging

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posted on 2021-01-05, 13:39 authored by Xiyao Liu, Yuying Sun, Jiahui Wang, Cundian Yang, Yayun Zhang, Lei Wang, Yan Chen, Hui FangHui Fang
The authenticity and copyright protection of volumetric medical images has become extremely important when these images are distributed online for diagnosis and education purpose. Compared to the authenticity and copyright protection of conventional images, there are two additional challenges for protecting the volumetric medical images. On one hand, the content of the protected medical images must be distortion-free to ensure unbiased diagnosis. On the other hand, it requires enhanced distinguishability to avoid misclassification of non-protected images into the protected set because volumetric medical images of different persons in the same modality share similar visual structures. To address these issues, a novel multi-slice feature based zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging is proposed. In this scheme, ring statistics are deployed to guarantee both the watermarking distinguishability and robustness. In addition, an intra-slice variation based mechanism is designed to further enhance the watermarking distinguishability. Finally, a logistic-logistic system based chaotic map is used to ensure the watermarking security. Our experimental results demonstrate that the proposed scheme not only satisfies the lossless quality requirement but also ensures the watermarking distinguishability and robustness, which outperforms the state-of-the-art schemes.

Funding

National Natural Science Foundations of China [61602527, 61772555, 61772553]

Natural Science Foundations of Hunan Provincial [2020JJ4746, 2017JJ3416, 2018JJ2548]

State Scholarship Fund offered by China Scholarship Council, under Grant 201806375002

History

School

  • Science

Department

  • Computer Science

Published in

Signal Processing: Image Communication

Volume

92

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Signal Processing: Image Communication and the definitive published version is available at https://doi.org/10.1016/j.image.2020.116124

Acceptance date

2020-12-30

Publication date

2021-01-02

Copyright date

2021

ISSN

0923-5965

Language

  • en

Depositor

Dr Hui Fang. Deposit date: 2 January 2021

Article number

116124

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