A medical image watermarking method based on the combination of DTCWT and PSO optimization

Abstract

In the era of intelligent healthcare, medical images play a crucial role in remote diagnosis, disease detection, cloud storage, and data sharing. However, they are vulnerable to security threats such as data tampering, copyright disputes, and privacy breaches. Traditional digital watermarking algorithms struggle to balance imperceptibility and robustness, making them insufficient to meet the security requirements of medical image protection. Motivated by these challenges, this paper proposes an efficient, robust, secure digital watermarking scheme for medical images. The proposed method integrates Dual-Tree Complex Wavelet Transform (DTCWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) to extract image features. Particle Swarm Optimization (PSO) is then employed to adaptively adjust the watermark embedding parameters, enhancing imperceptibility and robustness. Moreover, Henon chaotic mapping is introduced to generate pseudo-random sequences for watermark encryption, further enhancing security. Experimental results show that the proposed method achieves a PSNR of 34.59 dB, ensuring good imperceptibility. Under various attacks, the extracted watermark maintains high robustness, with an average NC of 0.99 under Gaussian low-pass filtering (5


$$\times $$

×



5), 0.99 under JPEG compression (QF = 10), 0.97 under Gaussian noise, and 0.98 under rotation attacks (15


$$^{\circ }$$




∘




). Research shows that this method can effectively improve the secure storage and transmission capabilities of medical images, providing strong support for the security of medical image data in intelligent medical environments.