A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

• Main Authors: Jian Li, Zelin Zhang, Shengyu Li, Ryan Benton, Yulong Huang, Mohan Vamsi Kasukurthi, Dongqi Li, Jingwei Lin, Glen M. Borchert, Shaobo Tan, Gang Li, Bin Ma, Meihong Yang, Jingshan Huang
• Format: Article
• Language: English
• Published: BMC 2020-12-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: Image segmentation; Key region; QR code; Reversible data hiding; Selective encryption; Texture complexity
• Online Access: https://doi.org/10.1186/s12911-020-01328-2

Abstract Background Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. Methods In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. Results The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. Discussion Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. Conclusions As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.