# [PERSON] Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise
## 4 Experimental Results
When Figure 5 is examined carefully, it is seen that the logos hidden by the proposed RS-based method are highly resistant to salt and pepper noises of various densities. As the logo size increases, the hidden message symbols begin to spread to the whole image, and the logo is slightly distorted. Besides the visual evaluations of the extracted logo, objective evaluations were made. The SSIM value is calculated for the similarity of the stego image, as well as for the similarity of the logo. Table 5 shows the SSIM results of the logo extracted from CT, MRI, X-ray and US stego images for different noise densities. The values that the proposed RS-based method reaches at 10% noise density are not reached even in 1% noise density without RS. Therefore, the message hidden by the proposed RS-based method has a high resistance to noise. The bit error rate (BER) value is used to determine the rate of distortion in the hidden message, which indicates the ratio of the number of bits broken to the total number of bits. In Equation (17), a calculation is made for the embedded (\(L_{emb}\)) and extracted (\(L_{ext}\)) bit values of the M \(\times\) N size logo. Figure 4: The logo extracted from stego images with salt and pepper noise density of 1%. (**a**) no- RS method, (**b**) Proposed RS-based method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Noise Density**} & \multicolumn{4}{c}{**Proposed RS-Based Method**} & \multicolumn{4}{c}{**No-RS Method**} \\ \cline{2-10}  & **CT** & **MRI** & **US** & **X-ray** & **CT** & **MRI** & **US** & **X-ray** \\ \hline
0\% & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) \\
1\% & \(1\) & \(1\) & \(1\) & \(1\) & \(0.95\) & \(0.96\) & \(0.95\) & \(0.94\) \\
5\% & \(1\) & \(0.99\) & \(0.99\) & \(0.99\) & \(0.80\) & \(0.80\) & \(0.82\) & \(0.81\) \\
10\% & \(0.95\) & \(0.96\) & \(0.97\) & \(0.96\) & \(0.70\) & \(0.72\) & \(0.70\) & \(0.68\) \\
20\% & \(0.76\) & \(0.80\) & \(0.79\) & \(0.79\) & \(0.58\) & \(0.56\) & \(0.58\) & \(0.59\) \\
50\% & \(0.32\) & \(0.31\) & \(0.34\) & \(0.33\) & \(0.29\) & \(0.29\) & \(0.28\) & \(0.30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SSIM value of secret message for logo size: \(45\times 45\). The higher the amount of distortion of the secret message, the higher the BER value; the lower BER value means better results. \[BER(\%)=100\times\left(\sum_{m=1}^{M}\sum_{n=1}^{N}\{L_{emb}(m,n)\oplus L_{ext}(m,n)\}/(M\times N)\right) \tag{17}\]  
Figure 5: Extraction of hidden message in stego MRI image for different logo size. (**a**) Salt and pepper noise density, (**b**) Proposed RS-based method, logo size: \(45\times 45\), (**c**) no-RS method, logo size: \(45\times 45\), (**d**) Proposed RS-based method logo size: \(64\times 64\), (**e**) no-RS method logo size: \(64\times 64\). Table 6 shows the BER value in the logo, retrieved from the stego images. According to Table 6, the message hidden by the no-RS method was extracted with a higher error rate than the message hidden by the proposed RS method. Despite the noise, the message hidden by the proposed RS method can be obtained with very few errors. The results of the proposed RS-based method and no-RS methods for all images are given in Tables 5 and 6. There are two more striking aspects in the results in Table 6. First, the hidden message in data hiding by the proposed RS-based method is almost error-free, even at 10% density noise. However, in hiding without RS, the message is erroneously extracted, even at the lowest noise. Second, the proposed method gives very superior results at all noise values. However, when the noise density approaches 50%, the values of the proposed method begin to decrease very much. The main reason for this is that loud noise completely changes the RS codewords. This is because it is not possible to extract the correct information in the completely distorted codewords.