Tamper Detection and Inpainting for Medical Images Using Recoverable Watermarking Techniques

• Main Authors: Shao-Kang Yeh, 葉紹康
• Other Authors: Shaou-Gang Miaou
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/69778707940437998609

碩士 === 中原大學 === 電子工程研究所 === 97 === A modern electronic patient record contains personal information and health history of a patient, including name of the patient, physical examinations, prescriptions, historic pathology, laboratory examination reports, treatment procedure, diagnostic images, and so on. Diagnostic images, also called medical images, can be modified very easily by using an ordinary image processing software in a computer. Hospitals, insurance companies, as well as patients might want to modify medical images for various reasons. The tampered images may be used illegally and cause significant losses and troubles for an individual or organization. In the worst case, malicious people can easily modify these medical images illegally without being discovered. Therefore, how to detect a tampered image effectively is an important problem that deserves serious investigation. For this problem, this study attempts to propose a solution that combines both the fragile and robust watermarking techniques, and each medical image is divided into region of interest (ROI) and region of no interest (NROI) according to their diagnostic importance. A fragile watermark is embedded in ROI, and the tamper detection and recovery information is also embedded in ROI. This information is used for data integrity verification, such as the detection of local modification, without having to compare with an original image. A watermarked image could be converted into its corresponding original image near losslessly after the extraction of embedded data in ROI. A robust watermark is embedded in NROI, which has no impact on a doctor’s diagnosis. The proposed robust embeding method is robust enough to resist image processing and compression attacks, and the original image has no visual quality degradation after recovery. Experimental results show that the proposed method can detect the tampered locations of the medical image, and successfully recover the original image pixels with almost no distortion in ROI. The proposed system can fix tampered portions in the image after tamper detection, and provide enhanced security and integrity for the distribution of medical image data.