The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods.

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Digital Image watermarking is usually used for identifying the owner, provider or recipient of an image. Watermarking also can be used for other applications such as copyright protection, fingerprinting, authentication, integrity verification, broadcast monitoring and content description. Reversibility is one of the important aspects in digital image watermarking. It is also known as lossless or invertible watermarking. Compared to conventional watermarking scheme, reversible data hiding restores not only the watermark, but also the original multimedia perfectly, which is a critical requirement for medical and military applications. In this work, after presenting different classifications of reversible methods, two commonly used reversible techniques are explained and their strengths and weaknesses are discussed.

This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.