- Enhanced CT Image Reconstruction Using VMD-Based Quaternion Bilateral Filtering
- Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT security
- Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2 norm, SSIM, and CNN-based classification with ResNet50
- A novel approach for CT image smoothing: Quaternion Bilateral Filtering for kernel conversion
- Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques
- Quaternion double random phase encoding for privacy-preserving cancelable biometrics
Research / Computational Neuroscience
Mahmoud Nasr
PhD Student in Computational Neuroscience
Mahmoud Nasr is a PhD student at the Sano Centre for Computational Medicine and AGH University of Science and Technology, where he conducts interdisciplinary research at the intersection of medical image processing, computational mathematics, and intelligent healthcare systems. His work is dedicated to advancing state-of-the-art image enhancement and analysis methods that improve the diagnostic value of clinical imaging modalities. By developing innovative algorithms grounded in signal processing theory and quaternion-based mathematical modeling, he aims to extract meaningful structural and statistical information from complex biomedical data.
His research interests extend to generative modeling and privacy-preserving computational frameworks, reflecting a commitment to both technological innovation and data security in medical applications. Mahmoud’s contributions focus on creating robust, interpretable, and clinically applicable imaging solutions that bridge the gap between theoretical algorithm design and real-world healthcare deployment. Through this work, he seeks to support clinicians and researchers with reliable computational tools that enhance decision-making, improve image-driven diagnosis, and promote secure biomedical data utilization.