Noise reduction in Clinical MRI Scans employing Filter Combining Techniques

Abstract

The implementation of Magnetic Resonance Imaging (MRI) pictures in the initial identification and treatment of a variety of disorders has become integral. These images are a set of data intended for visual inspections that are susceptible to specific noises and artefacts. Noise free MRI images are necessary for increasing the overall accuracy and clarity of assessment and therapy analytical evaluation. While gathering, processing and distribution many clinical images are influenced by various forms of sounds, resulting in the degradation of details pertaining with the image, which can impact the performance of illness treatment. To decrease the noise in medical scans for subsequent assessment, numerous filtering techniques are applied. The variety of digital filters, namely the Anisotropic filter, Median filter, Wiener filter and Non-Local Mean filter are discussed in this paper and their combinations are implemented with respect to all static parameters such as the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Universal Quality Index (UQI). A noise removal strategy based on the Wiener filter is developed in this study analysis for enhancing the image quality of diverse diagnostic imaging. The optimum outcome is obtained by combining the Wiener filter with all the static parameters. Existing noise reduction filtering approaches are outperformed by the suggested method.