# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 5 Discussion  
The paper introduces an innovative watermarking model specifically designed for medical images, using the Frequency Domain Curvelet Transform (FDCuT). This method segments medical images into different frequency sub-bands, providing an effective way to embed watermarks. The paper also includes a comprehensive comparative analysis, using metrics like Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity, to demonstrate the superiority of the proposed algorithm over existing methods in terms of securing medical images. The algorithm is carefully designed to balance robustness, imperceptibility, and computational efficiency. Additionally, the approach integrates RSA encryption, enhancing the security of medical images by adding a layer of public-key cryptography, which protects sensitive data from unauthorized access and maintains the confidentiality of embedded information.  
Table 1 represents Peak Signal-to-Noise Ratio values for different medical imaging modalities, such as CT, MRI, ultrasound (US), and X-ray, obtained from various algorithms, including the proposed system and reference algorithms [34; 35; 36; 37].  
In the context of the PSNR values, which serve as a measure of image quality, the superiority of the proposed algorithm can be assessed based on the higher PSNR values it achieves compared to the reference algorithms across the different medical modalities.  
Therefore, based on the PSNR values, the proposed algorithm demonstrates its superiority by consistently achieving higher PSNR values across all medical imaging modalities compared to the referenced algorithms. This suggests that the proposed system is more effective in maintaining image fidelity, preserving signal quality, and reducing noise in the resulting medical images for CT, MRI, ultrasound, and X-ray modalities.  
Table 2 displays the maximum embedding capacity values for different medical imaging modalities, including CT, MRI, ultrasound (US), and X-ray, as achieved using var  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed System** & [37] & [34] & [36] & [35] \\ \hline CT & 62 & 58 & 55 & 57 & 60 \\ MIRI & 68 & 62 & 61 & 63 & 64 \\ US & 65 & 55 & 54 & 55 & 61 \\ XRAY & 73 & 59 & 58 & 63 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the proposed algorithm and four other algorithms in terms of Peak Signal-to-Noise Ratio.  
Figure 6: Maximum embedding capacity.  
ious algorithms--the proposed system (Proposed System) and several reference algorithms [34; 35; 36; 37].  
In evaluating the superiority of the proposed algorithm based on the maximum embedding capacity values, the proposed algorithm demonstrates its superiority by consistently providing higher capacities for steganographic data hiding across all medical imaging modalities (CT, MRI, ultrasound, and X-ray) compared to the reference algorithms. This suggests that the proposed system allows for a greater capacity to embed additional data within the images without significantly degrading the quality or perceptibility of the original content.