DCT & DWT based watermarking scheme for medical information security. (April 2021)

• Record Type: Journal Article
• Authors: Fares, Kahlessenane; Khaldi, Amine; Redouane, Kafi; Salah, Euschi
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Medical image watermarking -- Electronic patient record -- Discrete wavelet transform -- Discreet cosine transform -- Schur decomposition; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102403
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The proposed watermarking approach in this work is a hybrid scheme based on a discrete wavelet transforms and Schur decomposition. The extraction of the mark is done without using the original image, the watermarked image alone will be sufficient to identify the patient. The integrity of the extracted information can be verified by comparing the hash embedded in the image. These schemes allow the protection of the patient's information and thus ensure the confidentiality of personal data. Abstract: In order to secure the exchanged medical images in telemedicine, we propose in this work two blind watermarking approaches for the medical images protection. In the first scheme a combination of DCT and Schur decomposition is performed. In order to obtain a good compromise between robustness and imperceptibility, the integration is performed in the medium frequencies of the image. In the second scheme, the combination of DWT and Schur decomposition provide a more robust watermark distribution. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality watermarked images and are very robust against several conventional attacks. These schemes allow the protection of the patient's information and thus ensure the confidentiality of personal data.