# [PERSON] Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise
## 4 Experimental Results  
The images were taken from the open source European Society of Radiology database [28]. The images are shown in Figure 3 and all are in 480\(\times\)480 resolution. For ease of comparison, the gray logo of Kocaeli University is used as a secret message instead of a text message. The gray logo was set in different resolutions, hidden in medical images, and the test results were examined. In the proposed method, [PERSON] codewords are produced by RS(7, 3). The primary purpose of this study is to retrieve the secret logo right from the noisy environment. However, it was also tested whether the visual quality of the proposed data hiding method gives  
Figure 3: The medical images used for data hiding. (**a**) CT, (**b**) MRI, (**c**) US, (**d**) X-ray. good results. First of all, stego images of the proposed method were evaluated in terms of quality parameters. For this purpose, the quality criteria such as mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity (SSIM) were examined in stego images. Equation (14) shows MSE calculated with \(R\times C\) size original (\(l_{o}\)) and stego (\(l_{s}\)) images, and Equation (15) shows PSNR. \[MSE=\sum_{r=1}^{R}\sum_{c=1}^{C}\left[ \begin{array}{c}I_{o}(r,c)-I_{s}(r,c) \end{array} \right]^{2}/(R\times C) \tag{14}\]  
\[PSNR=20\times log_{10}\left(255/\sqrt{MSE}\right) \tag{15}\]  
Equation (16) shows the structural similarity index (SSIM). If the mean values for original and stego images \(\mu_{o}\) and \(\mu_{s}\), variances are \(\sigma_{o}\) and \(\sigma_{s}\), and covariance is \(\sigma_{os}\), then SSIM is calculated as,  
\[SSIM=\frac{(2\mu_{o}\mu_{s}+C_{1})(2\sigma_{os}+C_{2})}{(\mu_{o}{}^{2}+\mu_{s} {}^{2}+C_{1})(\sigma_{o}{}^{2}+\sigma_{s}{}^{2}+C_{2})} \tag{16}\]  
where the C\({}_{1}\) and C\({}_{2}\) coefficients are small constants. If two images are the same, then SSIM is equal to 1. Table 1 shows the results of the proposed method for different embedding capacity in terms of bit-per-pixel (bpp). In the Table 1, it is seen that performance results of same type stego images are almost the same. The fact is that, it is deduced that results do not change too much when the number of images increases. Therefore, for simplicity, the rest of the paper shows the result for the images in the first column of Figure 3.  
Table 2 shows the average performance values of all images for different capacity sizes. The PSNR value is above 35 dB and the SSIM value is close to 1 in the images without any compression process; this shows that the visual quality does not change too much after hiding. When Table 2 is examined, it can be seen that the data hiding into the unit digit of the pixel of the cover image gives acceptable results. In Tables 3 and 4, the quality values of stego images of the proposed method were compared with existing medical data hiding methods. In most of the medical data hiding methods extracted, message quality is more important than high embedding capacity. So, experimental results were given for the same embedding capacity for a fair comparison. On the other hand, the hidden message is extracted with similar accuracy from noisy stego images for all capacity values. In Table 3, PSNR and SSIM values are given for the stego MRI image, with 0.79 bpp or less embedding capacity. The proposed method provides superiority to the other two methods in terms of PSNR. Although [3] study is in good condition in terms of SSIM, the amount of hidden data is low. It is obvious that the [3] method will yield much lower results than the proposed method for 0.79 bpp capacity.