Exploratory MRI Image Analysis to Improve Medical Imaging and Diagnostics

This article presents an exploratory analysis of knee MRI data to improve the quality of medical imaging and diagnosis. The study applied various noise filtering techniques including median, Gaussian, bilateral filters, and wavelet transform to minimize noise and artifacts, improving the quality of images for further analysis. The pixel intensity distribution analysis was performed, revealing the differences between normal and pathological tissues. These results highlight the importance of data preprocessing to improve diagnostic accuracy and to create efficient algorithms for automated medical image processing using deep learning methods.