A Comparative Study of Binary and Fibonacci Decomposition Watermarking for Medical Images

Abstract

This study addresses the critical challenge of embedding additional information into medical images, specifically focusing on the trade-off between watermark capacity and visual quality. The importance of this challenge lies in maintaining the diagnostic value of medical images while securely embedding auxiliary data such as patient identifiers or copyright information. The study conducts a comparative analysis of two watermarking schemes: binary decomposition and Fibonacci decomposition. The binary and Fibonacci decompositions were specifically applied by utilizing modified binary watermarks and leveraging the specific domain properties of the host medical images to minimize disruptions during the embedding process. The evaluation was performed on a dataset of brain magnetic resonance imaging (MRI) images. The watermark capacity was varied to assess its impact on visual quality, which was quantified using Peak Signal-to-Noise Ratio (PSNR). The results demonstrated that the Fibonacci decomposition method achieved a higher watermark capacity of up to 3.5 bpp while maintaining high visual quality, with an average PSNR value of 76.5 dB. These results indicate that the Fibonacci decomposition approach offers significant advantages in achieving a balance between high capacity and minimal image distortion, making it a promising solution for medical image watermarking applications.