Chapter 4 - Robust watermarking algorithm based on multimodal medical image fusion

Abstract

Over recent years, the volume of big data has drastically increased for medical applications. Such data are shared by cloud providers for storage and further processing. Medical images contain sensitive information, and these images are shared with healthcare workers, patients, and, in some scenarios, researchers for diagnostic and study purposes. Multimodal image fusion is the process of merging information from two or more image modalities into a single composite image that is better suited for diagnosis and assessment. However, an increasingly serious concern is the illegal copying, modification, and forgery of fused medical records, especially after the COVID-19 pandemic. In this chapter, we propose a robust and secure watermarking algorithm based on multimodal medical image fusion. First, we use nonsubsampled shearlet transform-based fusion to fuse MRIs and CT scans to obtain a fused mark image. This fused mark image has rich information and is better suited for diagnosis and assessment than an individual image. Furthermore, a combination of integer wavelength transform, QR, and singular value decomposition is utilized to perform an imperceptible marking of the fused image within the cover media. Additionally, an efficient encryption algorithm is performed by utilizing a 3D chaotic map on a marked image to ensure better security. Experimental outcomes on Kaggle and Open-i datasets indicate better resistance against a wide range of attacks. Lastly, the obtained results indicate that the proposed algorithm outperforms various other state-of-the-art techniques.