# Automatic Selective Encryption of DICOM Images
## 5 Conclusions and Future Work  
In this paper, a selective encryption approach was adopted to provide an efficient encryption mechanism for the pixel data of DICOM images in the spatial domain, which makes a trade-off between the processing time of the encryption process while sustaining the robustness of the encrypted DICOM images against different attacks. However, the selective encryption approaches require efficient segmentation mechanisms. Hence, in this paper, medical images' statistical properties were exploited to achieve this efficient segmentation. Then, in order to further reduce the processing time, this paper presented the use of lossless compression alongside efficient segmentation approaches. This paper adopted a selective encryption concept since it has a significant potential to shorten the processing time, by efficiently utilizing the data structure of image compression, optimization, and processing techniques to reduce the volume of data during encryption. This paper proposed an efficient selective encryption approach for multi-frame and single-frame DICOM object. The proposed approach for single-frame DICOM image was successfully implemented, tested, and evaluated. However, the presented approach seems to be complicated, and the saving time with respect to the two-region encryption approach [25] was small (with added compression step). Hence, it is believed that splitting the ROI into three regions might overcomplicate the selection mechanisms. While the proposed approach for multi-frame DICOM object was more robust and efficient as it significantly reduced the encryption time, and it was ~10,000 times faster than the Naive encryption approach, with a remarkable compression ratio due to the idea of improving the redundancy within the different frames of the DICOM object. Based on that, this proposed approach could be enhanced to be simpler for marketing goals. Further, increasing the dataset for the training stage could provide more accurate thresholds for any new input \begin{table}
\begin{tabular}{c c} \hline \hline
**Encryption Approach** & **Encryption Speed(Frame/s)** \\ \hline Frame-Based SE & 10,572 \\ Holistic-Based SE & 30.25 \\ Naive Encryption Approach & 24.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Encryption speed of the multi-frame encryption approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Encryption Approach** & **Original Data Size (MB)** & **Encrypted Data Size (MB)** \\ \hline Frame-Based SE & 457.06 & 3.12 \\ Holistic-Based SE & 457.06 & 406.23 \\ Naive Encryption Approach & 457.06 & 204.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The original and encrypted data sizes. DICOM dataset for hospitals and medical institutions. Furthermore, the high speed and efficiency of this approach could be utilized for encrypting 3D and colored DICOM images. As mentioned in the Introduction, there are different security requirements in the cybersecurity field, which can be accomplished through the proposed approaches by combining them with a more comprehensive framework such as the framework presented by [24; 47]. The proposed approaches are selective encryption mechanisms that facilitate security functionality when they are used in a holistic framework that supports these security requirements. Therefore, it is desirable to extend the presented work in order to achieve confidentiality, authenticity, and integrity. Based on the developed approaches in this paper, it was noticed that the combination of lossless compression with an efficient segmentation algorithm could significantly improve the performance of selective encryption. Therefore, in future research, it will be conducted to integrate the encryption and compression at a lower structural level to attain faster encryption/compression processing time. Methodology, Q.N. ; Writing--original draft, Q.N. ; Writing--review & editing, A.S., D.Z. and E.E. ; Supervision, A.S. and E.E. ; Funding Acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.