Publication

Abstract :

Medical image processing demands essential image restoration techniques to handle both blurred and noisy images. The image capture process frequently causes these degradations. The restoration of medical images such as MRI scans holds essential value for better diagnosis and improved treatment accuracy. The implementation of GPU parallel computing techniques together with optimization of memory and threads leads to faster computation. A combination of texture-based image analysis with advanced computational algorithms powers the automated filtration process. The approach uses forward selection to identify 98 texture attributes while refining the selection process to find optimal regularity features. A two-phase classification system trains automation parameters using artificial neural networks together with support vector machines. Research findings show that trilateral filtering yields superior noise reduction alongside better definition of MR image features than alternative techniques.