GPU Accelerated Trilateral Filter for MR Image Restoration

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. MRI serves as a non-invasive imaging tool to diagnose brain diseases yet the noise produced during its acquisition phase degrades image quality so that diagnosis accuracy suffers. Reduction of noise becomes essential for both clinical diagnostic procedures and computer-assisted medical analysis practices which require tissue classification and registration and segmentation algorithms. Detecting and eliminating noise from magnetic resonance images represents a complex medical imaging challenge. Multiple noise filtering methods operate on MR images with distinct performance benefits. This work presents a trilateral filter specifically designed for image restoration which provides both precision and speed performance. The bilateral filter served as foundation to develop this filter while adding another intensity comparison function to deal with brain MR image noise. A weighting control function based on intensity entropy is established for intensity-based weight calculations. 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.