Browsing by Author "Benyoucef, Aicha"

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Biomedical security : performance study and analysis
(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2024) Benyoucef, Aicha; Hamadouche, M'Hamed(Directeur de thèse)
This thesis investigates the development of a robust approach for securing medical data through watermarking techniques, with a specific focus on the application of QR code encryption. The research addresses the pressing need for improved security measures in medical data transmission and storage, considering the vulnerability of patient information to unauthorized access and manipulation. Through a comprehensive literature review and analysis of existing methods, the thesis identifies key challenges in medical image watermarking, including limitations in payload capacity, imperceptibility, and robustness against attacks. To address these challenges, the research proposes a novel watermarking approach that leverages QR code encryption to enhance both security and capacity within medical images. The methodology involves embedding QR code representations of Medical Imaging Test Reports (MITR) into the non-interest regions of medical images using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques. Evaluation of the proposed method is conducted using performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalization Coefficient (NC). The results demonstrate significant improvements in payload capacity, imperceptibility, and security against various attacks compared to existing watermarking methods. The pro- posed approach offers a balance between security requirements and practical considerations, making it suitable for real-world applications in medical data transmission and storage. Overall, this research contributes to advancing the field of medical image watermarking and lays the foundation for future developments in biomedical security
High-capacity DWT-SVD watermarking for MRI images embedding MITR medical information
(Elsevier, 2025) Benyoucef, Aicha; Goudjil, Aya; Hamadouche, M'Hamed; Boutalbi, Mohammed Chaker; Ammar, Mohammed; El Habib Daho, Mostafa
Securing Medical Imaging Test Reports (MITRs) during digital transmission is a growing concern in the era of telemedicine. Conventional watermarking methods often face a trade-off between imperceptibility, robustness, and payload capacity, especially in the context of sensitive medical data. To address this challenge, we propose an efficient and secure watermarking technique tailored for MRI brain images, using a combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The core idea involves embedding four sub-watermarks—a QR-encoded MITR, patient photo, and hospital logos—into strategically selected Region of Non-Interest (RONI) blocks of the cover image, while preserving the diagnostic Region of Interest (ROI). This region-based design ensures both high payload capacity and minimal visual distortion, even under hardware constraints. Experimental evaluations demonstrate that our method maintains high imperceptibility (PSNR > 67 dB, SSIM = 1.000), robustness (NC > 0.9430), and zero Bit Error Rate (BER = 0.1120) under common image processing attacks. Additionally, the use of QR codes for encoding the MITR improves the security and confidentiality of patient data. Compared to recent approaches, our method achieves better performance in both visual quality and robustness, confirming its effectiveness for secure medical image transmission in clinical and telehealth applications
Region-Based Medical Image Watermarking Approach For Secure EPR Transmission Applied to e-Health
(Springer, 2024) Benyoucef, Aicha; Hamaouche, M’Hamed
Digital health services have been more popular recently since it is simple to share electronic medical information via an open network. Several researchers have been interested in the problem of guaranteeing the confidentiality of these important records; they developed different methods of embedding this secret information in the shared medical image by watermarking techniques. One of the essential electronic patient records not being focused on in the security is the medical imaging test report (MITR), which is the radiologist’s interpretation and analysis of the medical images acquired during the examination. So the proposed approach is a region-based medical image watermarking for secure patient MITR. It is a new method based on embedding a QR code image of this MITR in the region of non-interest of the medical image in the frequency domain by applying the discrete wavelet transform and singular value decomposition techniques. The evaluation of the results of the proposed method using the following parameters PSNR (55.2132 dB), SSIM (0.9976), and NC (0.9999) is observed. It achieves high payload capacity, authentication, imperceptibility, and security against different attacks.
RONI-Based medical image watermarking using DWT and LSB algorithms
(Springer, 2022) Benyoucef, Aicha; Hamadouche, M’Hamed
In recent years, hiding information in medical images is the largest usage to secure this information or garnet the integrity of the owner, the embedding can distort the medical image and change the necessary health patient information. In this paper, we propose a robust method for medical image watermarking to secure patient data when it transmits in a non-secure channel. First of all, the original image is filtered by a sharpening filter for enhanced contrast then separated into two regions using snake segmentation. The embedding mark (Electronic Patient Record) is added to the frequency domain after applying Discrete Wavelet Transform (DWT) on the region of non-interest (RONI) using the last signification bit (LSB). This region has a predominantly black background; the region of interest (ROI) has the necessary patient information. This method preserves a high-quality watermarked image, and it improves imperceptibility, security, and authentication. Our method is evaluated by Pick Signal to Noise Ratio (PSNR = 46.4039 for 512 * 512 bits image size), SNR, NC, and histogram analysis, and it’s compared with existing schemes