Selective weights based median filtering approach for impulse noise removal of brain MRI images

In medical image processing, the accurate understanding and analysis of medical images is progressively challenging and demanding in providing the clear detection and diagnosis of the diseases. A very important and primary step in automatic detection of abnormalities in brain MRI images is reliable elimination of presence of the noise in the acquired images. Evaluation of the various noise removal algorithms to estimate the noise parameters is widely recognized as a typical standards for medical image processing solicitations. Validation of such brain MRI images can be done based on the evaluation through various statistical noise parameters. But the enactment of noise removal procedure profoundly depend on superiority of input brain MRI images and image processing skills employed for restoring original quality of brain MRI image by eliminating noises present in it and make the input image free from noises. This paper projects the type of noise existing in the brain MRI images and also removal of noises from the images using different types of noise elimination filters. The work proposes the design and implementation of the simple and efficient filtering technique for removal of noises in medical images specifically for brain MRI images and to upsurges the performance capabilities of the method. The experimental outcomes of suggested filtering algorithm are compared with the additional existing filtering approaches. The simulated results of filtering process executed on a standard set of assessment images shows that proposed algorithm provides good results with high SNR and low MSE values for the noise level of 95% and outperforms the conventional steps for removal of noise present in brain MRI images.