AI-Ready MRI Enhancement using Hybrid Preprocessing & Quality Evaluation Techniques

This study introduces an integrated framework of traditional and advanced enhancement algorithms to address common Magnetic Response Imaging (MRI) challenges such as noise, limited resolution, shadowing, etc., Our approach focuses on preserving Structurally important information while eliminating unwanted artifacts and shadowing that obscure diagnostic details. The methodology implements a multi-stage Python- based enhancement pipeline to enhance MRI images. The inspiration behind this project stems from the requirement for reliable and effective pipeline to enhance MRI images for better visualization and better-quality diagnostic accuracy. Traditional enhancement techniques often address one issue at a time, such as noise reduction or contrast adjustment. In this research, we aim to develop a unified pipeline that combines multiple enhancement techniques to significantly improve overall image quality. The aim of this project is to revive and examine MRI enhancement techniques. The core objective is to implement a hybrid pipeline incorporating several filters and correction methods and to evaluate the effectiveness of the enhancement through visual and quantitative metrics.