To filter an image is necessary in the preprocessing of images for the extermination of noise. Noise is an important factor that affects the information or details in the input image and so it is a need to remove it. Here, we examine a diversified amount of denoising approaches of MRI images by reviewing the positivity and weakness of every method and the well – suited method for the removal of different noises that tend to appear in the MRI images have been discussed. In this paper, we study about different noises of MRI images like Gaussian noise, Rician noise, Speckle noise, Salt and Pepper and the Poisson noise. Also we discuss about some of the basic error metrics like mean squared error, mean absolute error and the peak signal to noise ratio. Furthermore, we have presented a study about the filters like Mean filter, Median filter, Gaussian filter, Wiener filter, Anisotropic Diffusion, Lee and Frost filter, Non Local Means filter and neural network filters in order to eliminate the noises of the MRI images. The advanced technique using neural network tops the list as it follows the training approach. Each of these filters is better in some specific manner and so hybrid filters with the better features of these filters will provide greater accuracy and robustness.