An efficient reversible data hiding using SVD over a novel weighted iterative anisotropic total variation based denoised medical images. (April 2023) - Diwakar, Manoj; Kumar, Pardeep; Singh, Prabhishek; Tripathi, Amrendra; Singh, Laxman
Abstract: Computed tomography (CT) advancement and extensive usage have raised the public's worry regarding the patient's associated radiation dose. Reducing the radiation dose may lead to more noise and artifacts, which may harm the reputation of radiologists. The instability of low-dose CT reconstruction necessitates better image reconstruction, which increases the diagnostic performance. More modern low-dose CT tests have demonstrated outstanding results. Many times these low-dose denoised medical images with medical related information are also required to transmit over a network. Hence in this article, firstly is a novel denoising method is proposed to improve the quality of low-dose CT images that is based on the total variation method by utilizing whale optimization algorithm (WHA). WHA method is important for getting the best possible weighted function. Reduction of noise occurs by the comparison of a given output to the ground truth, although total variation tends to statistically migrate the data noise distribution from strong to weak. Following denoising, a reversible watermarking approach based on SVD and multi-local extrema (MLE) approaches is provided. Individual results of denoising and watermarking are excellent in terms of visual and performance metrics, according to comparative experimental investigation. Also it was also analyzed that if the watermark is embedded over the denoised CT images then the results of watermarking methods are impressive. So, resultant image offers us the chance to use our visual perception abilities to allow us to cut noise and keep vital and secure information. Highlights: A weighted total variation (TV) is proposed for CT image denoising. Weight function is designed using Bilateral and whale optimization methods. SVD and MLE based watermarking over denoised CT images is performed. Comparative analysis is performed for proposed CT image denoising method. Watermarking over denoised and non-denoised images is analyzed.