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Ensuring the confidentiality of medical images during storage and transmission is a critical challenge in modern healthcare. This study proposes a novel hybrid image encryption framework that integrates a memristor-based chaotic system, DNA-inspired operations, Add-Rotate-Xor (ARX), and Triple Data Encryption Standard (3DES). A four-dimensional two-memristor chaotic circuit is analysed through phase portraits, Lyapunov exponents, and bifurcation diagrams to establish its suitability as a strong entropy source. Chaotic sequences generated from the system are digitized using a mod2 post-processing scheme and validated by NIST SP 800 − 22, FIPS 140-1, and Chi-square statistical tests, confirming high-quality randomness. The encryption framework combines chaotic diffusion and confusion, symbolic DNA crossover operations, ARX, and a 3DES whitening stage to provide multilayered security. Experimental validation on four medical image datasets—Bone Fracture, Breast, Retina, and Teeth—demonstrates that the scheme achieves near-ideal entropy values (~ 7.99), high NPCR (~99.6%), and UACI (~ 33%), while producing cipher images with noise-like characteristics and negligible structural similarity to the originals. Real-time implementation on the NVIDIA Jetson Nano and PYNQ-Z1 platform verifies the feasibility of the method for embedded medical applications. Comparative analysis indicates that the hybrid approach outperforms conventional chaotic or DNA-only schemes by leveraging the complementary strengths of chaos, bio-inspired computing, and classical cryptography. These findings confirm that the proposed framework offers a secure, efficient, and practical solution for protecting sensitive medical images against statistical, differential, and brute-force attacks.
Süzgen et al. (Mon,) studied this question.