Data Integrity of Radiology Images Over an Insecure Network Using AES Technique

Document Type : Research Articles


1 Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

2 Department of Information Science and Engineering, NIE Institute of Technology, Mysuru, India.

3 Independent Consultant, AI in Radiation Oncology, Bengaluru, India.

4 Research and Development, RTWO Healthcare Private LLP, Mahalakshmipuram, Bengaluru, India.


Background: While transmitting the medical images in radiology information systems the adversary effect can break the CIA (Confidentiality, Integrity, and Availability) triads of information security. The objective of the study was to transmit the complete set of image objects in a dataset without data integrity violation. Methods: In this paper a hybrid cryptographic technique which combines the prime details from the patient dataset (stack of axial 2D images) and the Advanced Encryption Standard (AES) method has been proposed. The steps include a) Creating an artificial X-ray image (DRR) from the 3D volume, b) dividing the DRR image in x and y directions equally into four regions, c) applying the zig-zag pattern to each quadrant, and d) encryption of each quadrant with block cipher mode using the AES algorithm. After dataset transmission the DRR image was regenerated at the receiver and compared each of the deciphered blocks (transmitted ones) using the histogram technique. Results: The technique was tested on CT and MRI scans of sixty datasets. The image injection techniques, such as adding and deleting an image from the dataset and modifying the image pixels, were tested. The results were validated statistically using mean square error and histogram matching techniques. Conclusion: The combination of the DRR and the AES technique has ensured the secured transmission of the entire dataset and not an individual image.


Main Subjects

Volume 22, Issue 1
January 2021
Pages 185-193
  • Receive Date: 06 October 2020
  • Revise Date: 24 December 2020
  • Accept Date: 12 January 2021
  • First Publish Date: 12 January 2021