# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 4 Results and Discussion  
Several experiments were performed using the proposed method with the datasets described in Section 3.1. The robustness of the original and stego images was measured through the calculation of the PSNR. The PSNR value indicates the quality of the stego image in comparison to the original cover image; a higher PSNR value suggests a higher quality stego image that closely resembles the original, while a lower PSNR value indicates a lower quality stego image with more noticeable differences from the original. The MSE is required for the PSNR calculation, which is determined using Equations (1) and (2). \[\text{MSE}=\frac{1}{\text{MN}}\sum_{m=0}^{\text{M-1}}\sum_{m=0}^{\text{N-1}} \left[\text{I}(\text{m},\text{n})-\text{I}^{\prime}(\text{m},\text{n})\right]^ {2} \tag{1}\]  
\[PSNR=10 log_{10}\left(\frac{(I_{max})^{2}}{MSE}\right) \tag{2}\]  
where \"\(I\)\" is the original image and \"\(I\)\" is the stego image. The highest achievable value of the original image is designated \(I_{max}\). The structural similarity between the original and stego images is assessed using the structure similarity index measure (\(SSIM\)) metric and multi-scale \(SSIM\) (MS-SSIM). The SSIM is calculated between the original image (\(I\)) and the steganographic image (\(I^{\prime}\)), which have the same dimensions of \(\text{M}\times\text{M}\) pixels size based on the contrast (\(c\)), structure (\(s\)), and luminance (\(I\)) using Equation (3). \[SSIM(I^{\prime},\ I)=\left(\frac{(2\mu_{I^{\prime}}\mu_{I}+c_{1})(2v_{I^{ \prime}I}+c_{2})}{(\mu^{2}{}_{I^{\prime}}+\mu^{2}{}_{I}+c_{1})(v_{I^{\prime}} ^{2}+v_{I}^{2}+c_{2})}\right) \tag{3}\]  
where \(\mu_{I^{\prime}}\) is the mean of \(I^{\prime}\) and \(\mu_{I}\) is the mean of \(I\), while \(v_{I^{\prime}}^{2}\) and \(v_{I}^{2}\) are variance of \(I^{\prime}\) and \(I\), respectively. \(2v_{I^{\prime}I}\) is the covariance of \(I^{\prime}\) and \(I\). Two stabilizer variables are used as \(c_{1}\) and \(c_{2}\), which are calculated based on the maximum pixel value multiplied by constant values (0.01 for \(c_{1}\) and 0.03 for \(c_{2}\)). The SSIM value closer to 1 indicates a higher level of similarity between the images, while a value closer to 0 indicates a lower level of resemblance between the images. MS-SSIM is an advanced variant of SSIM that evaluates image quality at multiple scales through a progressive down-sampling process, mirroring the multiscale processing mechanisms in the early visual system. This approach enables MS-SSIM to perform equally well or even better than SSIM, particularly when assessing the quality of multimodal images. Table 3 shows the results of the proposed steganography method using patient prescription text of various sizes, between 100 and 5000 characters, embedded into randomly selected medical images along with different LSB bits. The high values of PSNR indicate that the hidden message is highly imperceptible, and there is a high similarity between the stego image and the original. Table 4 shows the comparison results based on the average PSNR, MSE, SSIM, and MS-SSIM of the proposed steganography method using prescription text of various sizes between 100 and 5000 characters for all patients' multimodal images. Again, the high values of PSNR indicate that the hidden message is highly imperceptible, and there is a high similarity between the stego image and the original. SSIM values in Table 4 show that all SSIM values are close to 1, indicating that the stego image and original image are almost identical. The MSE value closer to 0 indicates that the stego and original images are almost identical in a pixel-by-pixel comparison. The degree of imperceptibility can be evaluated by visually inspecting the quality of the stego image. Figure 5 shows the original and stego images with different patient prescriptions hidden ranges in text size between 100 characters and 5000 characters. Visual examination clearly shows that the structure of the original image is preserved even after hiding the text.