Master's Thesis at the College of Computer Science and Information Technology Discusses Denoising Medical Images Using Total Variation Minimization Techniques

The College of Computer Science and Information Technology discussed the Master's thesis titled "Denoising Medical Images based on Total Variation Minimization Techniques."

The study, presented by student Saifuddin Sabah Mahmoud under the supervision of Dr. Shihab Ahmed Ibrahim, aimed to enhance the quality of brain MRI images through innovative techniques for reducing noise and distortions and to address challenges associated with the Total Variation (TV) model. It aimed to overcome the deficiency in deriving points where the variation is zero, thereby simplifying optimization solutions.

Experiments showed the superiority of the proposed method in terms of PSNR and MSSIM metrics compared to modern approaches, confirming its effectiveness in improving the quality of MRI images.

The study recommended integrating the proposed technique with Artificial Intelligence (AI) and Deep Learning to explore the possibility of enhancing denoising performance, particularly in dealing with complex noise levels, which opens up new horizons in the field of medical image processing.