Robust medical image watermarking based on Ridgelet transform and Ant Colony Optimization for telemedicine security

The development of telemedicine needs strong solutions to prevent manipulation of medical data while maintaining accurate diagnosis. Current watermarking methods aim to establish an effective compromise between robustness, imperceptibility, and payload capacity, particularly against geometric and compression attacks. This work presents a blind medical image watermarking approach that combines adaptive Quantization Index Modulation (QIM), Ant Colony Optimization (ACO), Ridgelet transform, and QR decomposition. While QR decomposition stabilizes coefficients against noise and compression, the Ridgelet transform isolates diagnostically important linear features. Adaptive QIM dynamically modifies quantization steps according to local texture complexity, while ACO optimizes embedding locations by reducing perceptual distortion. The method achieves a high payload capacity of 73,728 bits/image when tested on a brain tumor MRI dataset, enabling the smooth integration of authentication hashes and patient details. Imperceptibility is confirmed with PSNR = 48.63 dB and SSIM = 0.9917, ensuring minimal visual distortion, while robustness evaluations show strong resistance to common attacks such as noise, filtering, and JPEG compression, with Normalized Cross-Correlation (NCC) values above 0.99 for these scenarios. Furthermore, the method maintains practical computational efficiency (0.89 s embedding, 0.52 s extraction), highlighting its applicability for securing sensitive medical information while preserving interoperability and diagnostic reliability in telemedicine workflows. However, limitations include vulnerability to severe geometric distortions (rotation >75°, cropping >25 %), and future work will focus on integrating geometric-invariant features and optimizing computational efficiency for real-time applications.