Secure management of retinal imaging based on deep learning, zero-watermarking and reversible data hiding

Advances in communication and information technologies have allowed for improvements in the distribution and management of several types of imaging in digital medical environments. The scientific literature has reported data hiding methods that can contribute to improving medical image management and mitigate information security risks. This paper proposes a secure management scheme for retinal imaging based on deep learning, reversible data hiding and zero-watermarking. To create a proper link between a patient and their retinal image, a unique feature is obtained through retina vessel segmentation and optic disk detection using U-Net and RetinaNet deep learning architectures, respectively. The unique feature, in conjunction with a halftoned version of the patient's image, are employed to generate a zero-watermarking code using a zero-watermarking technique based on message digest, spread spectrum, and seam-carving methods. Finally, using a color channel of the retinal image, the zero-watermarking code is concealed in a reversible manner using a data hiding technique based on code division multiplexing. The proposed method ensures patient authentication and verification of integrity, and avoids detachment between the patient and their retinal image. Experimental results show the contribution of the proposed scheme to and its efficiency in retinal image management.