Reversible Data Hiding for Medical Images Using Boundary Expandable Schemes

The researches of Internet of things are getting more and more popular these years. To exchange secret data through the internet, the reversible data hiding technique plays an important role. As we know, since medical images generally consist of many pure black and white points, traditional reversible data hiding techniques encounter some bottlenecks in medical images. These points are called boundary points and they may cause the overflow and underflow problems to happen after data hiding. In this paper, we propose a new reversible data hiding method to solve these problems. The method is a hybrid scheme based on the one-dimension and two-dimension difference expansions. We introduce an efficient classification to interchange the expansion schemes. In addition, we introduce boundary expandable schemes. We demonstrate the effectiveness of the proposed method across a wide range of medical images. Compared with the previous methods, the proposed one has higher hiding capacity, higher image quality, and less size of location map. To get further applications precisely, we also demonstrate the proposed scheme on a mobile device to show its application on the internet of healthcare.