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.

当在线分发用于诊断和教育目的的大量医学图像时，其真实性和版权保护已变得极为重要。与常规图像的真实性和版权保护相比，在保护体积医学图像方面存在两个额外的挑战。一方面，受保护的医学图像的内容必须无失真，以确保正确的诊断。另一方面，由于在同一模态中不同人的体医学图像共享相似的视觉结构，因此需要增强的可区分性，以避免将未受保护的图像误分类为受保护的集。为了解决这些问题，提出了一种新颖的基于多层特征的零水印方案，该方案具有更高的可分辨性和鲁棒性，可用于体积医学成像。在该方案中，部署了环统计以确保水印的可区分性和鲁棒性。另外，基于片内变化的机制被设计为进一步增强水印区分性。最后，采用基于逻辑-物流系统的混沌图来保证水印的安全性。我们的实验结果表明，提出的方案不仅满足无损质量要求，而且确保了水印的可区分性和鲁棒性，其性能优于最新方案。基于切片内变化的机制被设计为进一步增强水印可分辨性。最后，采用基于逻辑-物流系统的混沌图来保证水印的安全性。我们的实验结果表明，提出的方案不仅满足无损质量要求，而且确保了水印的可区分性和鲁棒性，其性能优于最新方案。基于切片内变化的机制被设计为进一步增强水印可分辨性。最后，采用基于逻辑-物流系统的混沌图来保证水印的安全性。我们的实验结果表明，提出的方案不仅满足无损质量要求，而且确保了水印的可区分性和鲁棒性，其性能优于最新方案。