A Effectual Technique of Impulse Noise Suppression for Assessing the Impact of Brain Disorders in MRI by applying Selective Weighted Median Filter

Abstract

An accurate analysis of medical images is progressively demanding in providing the absolute detection and diagnosis of diseases in medical imaging. The significant pre-processing step in MRI data processing is noise elimination. Noise deletion is essential step to increase image quality and performance of all the tasks desirable for quantitative imaging analysis. In this paper a new scheme for impulse noise removal in corrupted MRI brain images is introduced. The proposed scheme is a simple & efficient filtering technique that effectively detects and removes the salt and pepper noise. The experimental results of suggested noise purifying process executed on standard set of assessment images shows that algorithm provides a very good results with low mean-squared-error and high signal-to-noise ratio values for noise density up to 95% and outperforms significant tradeoff between fine detail preservation and noise removal in brain MRI images.