# [PERSON] Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise
## 4 Experimental Results
In Figure 6, the BER and SSIM results of the hidden message extracted from the stego image for different salt and pepper noise intensities are presented with comparative graphics. Finally, the proposed method was compared with the existing methods in this study. In Table 7, the BER values of the secret message are compared with similar methods in the literature. All of the methods in Table 7 evaluated the extracted secret message from the stego image that contains salt and pepper noise, with a density of 1%. The proposed method recovers the message without loss at 1% noise density, while other methods give up to a quarter of the message losses. While the noise density is 1%, there are two methods with the BER value closest to the proposed method. In Table 8, a second comparison was made for these two methods for different noise densities. Table 8 shows the BER and SSIM comparison results of the secret message. When Table 8 is evaluated, it is seen that the proposed method has better BER values compared to [30]. At noise densities of 5% and 10%, the proposed RS-based method is superior to the [14] methods, which proposed that one uses a noise filtering block. Although the BER value of the proposed RS-based method at 20% noise density is superior, the SSIM value is lower. At 50% noise density, both methods give bad results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Noise Density**} & \multicolumn{3}{c}{**Proposed RS-Based Method**} & \multicolumn{3}{c}{**No-RS Method**} \\ \cline{2-9}  & **CT** & **MRI** & **US** & **X-ray** & **CT** & **MRI** & **US** & **X-ray** \\ \hline
0\% & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
1\% & 0 & 0 & 0 & 0 & 0.7 & 0.5 & 0.7 & 0.6 \\
5\% & 0.1 & 0.1 & 0.2 & 0.1 & 3 & 2.8 & 2.7 & 2.7 \\
10\% & 0.5 & 0.5 & 0.5 & 0.6 & 5.9 & 5.6 & 5.6 & 6 \\
20\% & 3.9 & 3.1 & 3.8 & 3 & 11.5 & 11.1 & 11.1 & 10.7 \\
50\% & 24.9 & 25.1 & 24.9 & 24.6 & 28 & 28.5 & 28.2 & 28.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Bit error rate (BER) (%) value of secret message for logo size: \(45\times 45\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise Density** & **Proposed RS-Based** & [13] & [14] & [15] & [30] & [29] \\ \hline
1\% & 0 & 8.2 & 0.15 & 5.1 & 0.14 & 28.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of BER (%) value of the secret message with existing methods. Figure 6: Evaluation of the retrieved secret message according to the salt and pepper noise density. **(a)** SSIM-Noise Density, **(b)** BER-Noise Density. Finally, the performance of the proposed method is tested against different noises. Three types of noises added to the stego images and test results were evaluated in terms of BER. The normal distributed Gaussian noise and uniform distributed additive uniform and multiplicative speckle noises are applied to the stego images with different variance sizes. The test results are given in Table 9. As stated above, the proposed method is resistant to an amount of noise densiy. So, Gaussian and uniform noises are set to one fifth of the stego images. The mean values for all noise types are zero. From Table 9 it is seen that the hidden message is extracted, with lower error for Gaussian and uniform noise. On the other hand, speckle noise affects stego images more than other noises. So, hidden messages in the CT and MRI are extracted correctly, while an important part of secret messages in the US and X-ray is lost. Therefore, the proposed RS-based method has also resisted against Gaussian and uniform noises for all images.