# Automatic Selective Encryption of DICOM Images
## 4 Performance Analysis and Discussion
### Approach II  
#### 4.2.1 Security Evaluation  
A benchmark set of 69 grayscale MRI Neoadjuvant Chemotherapy (NACT) breast cancer DICOM frames [46] is utilized to evaluate the proposed approaches experimentally. The statistical properties of the given dataset before and after encryption are depicted in Tables 3 and 4, respectively, which show a significant difference between them. Original images have a median value of around 0, due to a large number of pixels with zero intensity in the background, while encrypted images have large median values.  
The performance overview of the proposed approaches is illustrated in Table 5. The low correlation between the encrypted and original images has good robustness against linear attacks, while the low PSNR values of the proposed approaches display good randomness. The UACI of encrypted images using Frame-Based SE is smaller than the UACI of the images encrypted using the Holistic-Based SE. The NPCR value was almost the same for all approaches with a very high score (\(\sim\)100%), and this means that all pixels' values have been changed in both cases as designated by the NPCR score. The Frame-Based SE has a higher MAE value in comparison with the Holistic-Based SE value, which indicates that the encrypted images based on the Frame-Based SE are significantly different from the original images. Entropy is a measure of the uncertainty present in the encrypted image, and a higher entropy of the encrypted image indicates a higher degree of randomness. As the bit depth of the DICOM datasets used in this approach is 16, the entropy value varies between 0-16 bits/pixel. The entropy values for both approaches were almost the same.  
Histogram: This metric assists in distinguishing the correlation between the original and the encrypted image by showing each gray-level probability. Subsequently, if the difference between the original and encrypted image is large; the image is highly uncorrelated. Moreover, if the gray-level probabilities are distributed uniformly, the attacker cannot predict enough information to make a statistical attack by fitting the distribution [38]. Figure 12a shows the plain image, while Figure 13a shows its histogram. Figure 12b shows the encrypted image using Frame-Based SE, while Figure 13b shows its histogram. Figure 12c shows the encrypted image using Holistic-Based SE, while Figure 13c shows its histogram.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{3}{c}{**Pixel Intensity**} \\ \cline{2-4}  & \(\mu\) & \(\sigma\) & **Median** \\ \hline Frame-Based SE & 1358.45 & 745.62 & 1038.53 \\ Holistic-Based SE & 1739.14 & 940.98 & 2001.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical properties of the encrypted images (average of the given dataset).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Encryption Approach**} & \multirow{2}{*}{**CORR**} & \multirow{2}{*}{**Entropy (bits/pixel)**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**NPCR**} & **PSNR (dB)** & **UACI (\%)** \\ \hline Frame-Based SE & 0.00098 & 15.28 & 531,628.3 & 100 & 16.03 & 92.27 \\ Holistic-Based SE & 0.000088 & 15.08 & 421,078.6 & 100 & 16.13 & 93.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cryptanalysis results of the 69 encrypted frames.  
Figure 13a depicts the pixel intensity distribution, which is almost a Gaussian distribution. The encrypted frame using the Frame-Based SE shows a flat pattern, as shown in its histogram in Figure 13b. Additionally, the histogram of the encrypted frame based on the Holistic-Based SE has nearly a flat histogram, as shown in Figure 13c, which shows a good level of randomness. Hence, both approaches have a similar distribution.