# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 3 Proposed System  
The proposed system for securing medical images implements an organized sequence of processes to fortify the integrity and confidentiality of sensitive healthcare data, as shown in Figure 1.  
Initiating with medical images and the watermarking text, the algorithm systematically navigates through a series of strategic stages. These include reading the medical image and determining image dimensions, which are essential for watermark embedding. Leveraging specialized encoding schemes, textual information is converted into bits to serve as watermarks. The Algorithm 1, SecureMediMark algorithm then applies an advanced Frequency Domain Curvelet Transform (FDCuT), effectively dissecting the image into distinct frequency sub-bands crucial for subsequent watermark embedding and security measures. The process continues with block-based transformations, a Discrete Cosine Transform (DCT), and strategically embedding text watermark bits within transformed coefficients. Conclusively, encryption using the robust RSA algorithm fortifies the watermarked coefficients, ensuring the security and confidentiality of embedded information within the medical image. This amalgamation of diverse processes in the algorithm is designed to strengthen medical images against unauthorized access, ensuring the protection and privacy of the embedded information. FDCuT, or Fast Discrete Curvelet Transform, presents notable advancements over prior methods like DCT and wavelet transforms. Its effectiveness lies in superior directional information capture and increased sparsity, particularly advantageous for representing signals with intricate directional features, curves, and edges. Unlike DCT, FDCuT offers improved sparsity, overcoming the limitations of traditional methods in handling sparse data efficiently. While it is more computationally intensive than DCT, FDCuT showcases competitive efficiency compared to certain wavelet transforms, making it ideal for applications reliant on capturing directional features such as medical imaging and edge detection in images. Overall, FDCuT's ability to provide enhanced directional information, higher sparsity, and adaptability to specific application needs makes it a compelling choice for advanced signal processing tasks. \(I_{LF}\) represents the reconstructed low-frequency sub-band. \(I_{MF}\) represents the reconstructed medium-frequency sub-band. \(I_{HF\_Reconstructed}\) represents the previously reconstructed high-frequency sub-band after applying inverse DCT and block-based processing. Following the securing process, the system's decryption procedure employs the RSA decryption technique to retrieve and extract the watermark from the previously encrypted coefficients as shown in Algorithm 2 MediRestore decryption protocol. It proceeds by reverse block-based processing, applying inverse operations like the inverse DCT to reconstruct the original high-frequency (HF) sub-band, enabling the retrieval of the embedded watermark. Further, the algorithm employs the Frequency Domain Curvelet Transform (FDCuT) inverse to reconstruct the host (medical) image. Lastly, by utilizing specialized libraries or tools, the system visualizes and interprets the reconstructed medical image data, allowing for the restoration of the original medical image from its sub-bands. This reverse process effectively restores the original medical image, ensuring the reversal of the transformations previously applied and reconstituting the image to its pre-altered state, ensuring the preservation of the original information within [32]. Figure 1: SecureMediMark flowchart. ```
Step 1:Reading the Medical Image Input: Medical image file Action: Read the medical image Step 2: Determine Image Size and Convert Text to Watermark Bits Input: The read medical image and the text watermark Action: Determine Image Size: Identify the size (dimensions) of the host (medical) image.