Medical image encryption algorithm based on Chen’s chaotic system and the Advanced Encryption Standard algorithm

Background: Single Chen’s chaotic system or Advanced Encryption Standard (AES) cannot provide sufficient anti-attack performance when separately used for medical image encryption. This study built a hybrid encryption framework combining four-dimensional (4D) Chen hyperchaotic system, Arnold transform, logistic map and AES-256 to fix security flaws of single encryption modules and protect privacy of clinical medical imaging data.

Methods: A three-stage encryption workflow driven by logistic chaotic sequences was designed: (I) the logistic map ran 4,300 iterations (initial X=0.1, μ: 3.57–4.00, step =0.0001). The 100th and 1000th sequence values were Arnold mapping parameters; the 1200th, 1500th, 1800th and 2000th outputs served as four initial states of the 4D Chen hyperchaotic system to guarantee high chaotic parameter sensitivity; (II) Arnold transform realized pixel scrambling, followed by XOR diffusion with Chen hyperchaotic sequences to eliminate intra-image pixel correlation; (III) we extracted 256 continuous bits (positions 2050–2305) from logistic sequences as the unique AES-256 secret key, and implemented multiround block encryption under AES Electronic Codebook (ECB) mode to boost cipher randomness. Simulations were run on MATLAB R2019b with two self-generated medical phantom images: a 743×720 liver lesion phantom (46.1 kB) and a 236×180 splenic trauma phantom (24.6 kB).

Results: Quantitative metrics show excellent encryption performance. For liver lesion ciphertext, number of pixels change rate (NPCR) =99.66% and unified average changing intensity (UACI) =34.73%; splenic trauma ciphertext reached NPCR =99.81% and UACI =33.55%, both above the 99.6% and 33.3% security thresholds. Ciphertext χ2 values were 275.5194 (liver lesion phantom) and 243.3932 (splenic trauma phantom), much lower than plaintext values (1.4977×105, 4.4232×106), proving uniform grayscale distribution. Ciphertext information entropy was 7.9953 (liver lesion phantom) and 7.9969 (splenic trauma phantom), nearly the ideal maximum of 8.00. Ciphertext pixel correlation coefficients (horizontal, vertical, diagonal) ranged −0.0042 to 0.0039, close to zero and far smaller than plaintext coefficients (>0.93). Compared with 2025–2026 state-of-the-art chaotic encryption schemes, our method achieved the best NPCR, UACI and correlation results. Extra tests confirmed strong resistance against chosen-plaintext, chosen-ciphertext, noise and occlusion attacks, with stable efficiency for computed tomography, magnetic resonance imaging, ultrasound and X-ray multimodal medical images.

Conclusions: The proposed linked chaotic-AES hybrid algorithm unifies Arnold parameters, Chen hyperchaotic initial values and AES keys via logistic mapping, overcoming drawbacks of independent random module design. It achieves near-ideal randomness, ultra-low pixel correlation and strong multi-attack resistance versus standalone Chen chaos or AES. This lightweight, low-overhead scheme provides reliable end-to-end security for clinical medical image transmission and storage in smart healthcare systems.