A Robust Fuzzy Equilibrium Optimization-Based ROI Selection and DWT-Based Multi-Watermarking Model for Medical Images

Abstract

Image watermarking is the process of securely embedding a higher amount of information in the host object. These processes ensure authentication, image integration, and content verification. Several existing methods face complicated problems, such as security issues, robustness, and data leakage. Therefore, researchers developed specific methods for different applications. However, the performance of the currently obtained method was lower due to their low resistances. Therefore, to overcome this issue, we employed a novel technique, a fuzzy equilibrium optimization (FEO) approach, for embedding water image encryption. Initially, the raw image undergoes fuzzification to determine the critical point; thus, the intensity of the radial line selects a region of interest (ROI). Finally, the watermarking images are converted into a time-frequency domain via discrete wavelet transform (DWT), where the sub-band is converted based on value of magnitude. The proposed technique is analyzed using three medical image datasets, namely magnetic resonance imaging (MRI), ultrasound (US), and computed tomography (CT) datasets. However, all pixels in each sub-band are replaced to form a fully encrypted image, guaranteeing a watermarked reliable, secure, non-breakable format. Singular values are obtained for the encrypted watermarking image to provide high robustness to the watermarked image. After validation, the proposed fuzzy equilibrium optimization technique achieved higher robustness and security against different types of attacks. Moreover, the proposed FEO technique achieved a value of peak signal to noise ratio (PSNR) about 42.5 dB higher than other compared techniques.