A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images

Abstract

The protection of medical images transmitted through the E-healthcare system is very critical. Nowadays, medical image watermarking has been emerged as trustworthy way to authenticate medical information during transmission. This paper presents a secure self-embedding scheme for detection and correction of tamper in medical images. The proposed scheme involved two decomposition and dimensionality reduction techniques, singular value decomposition and learning sparse decomposition. First, the color medical image is transformed into YCrCb color space and the luminance plane is chosen. To create the watermark, the medical image is automatically classified into region of interest (ROI) and region of non-interest (RONI), and then, the ROI is encoded by sparse decomposition with learned BPDN dictionary. The sparse watermark is then hidden in the singular values of the host part of the image. The quantitative and qualitative results show that the proposed method is robust against numerous aggressive and geometric distortions without compromising the quality of the original medical image. The proposed algorithm yields a high PSNR larger than 45dB for all type of images, as well as high NC value under all types of attacks. It is demonstrated that the presented system performs better than the existing state-of-art techniques, and could be helpful for e-healthcare systems.