Evaluating Denoising Performances of Fundamental Filters for T2-Weighted MRI Images

In Magnetic Resonance (MR) images, noise is a common issue which limits the image accuracy of any quantitative measurements. Noise elimination in MRI image pre-processing is an important step to eliminate the noise and to make the image fit for further steps involved in the process of analyzing. However, different types of noises produces ranges of significant impact on image quality, and thus tend to affect human interpretation and performance of computer-aided diagnosis systems. Another issue is about filtering strategies to eliminate noise and preserve high quality image depending on filter reconstruction ability and noise model. In this work three different filtering algorithms such as Median filter (MF), Adaptive filter (ADF) and Average filter (AVF) are used to remove the additive noises present in the MRI images i.e. Gaussian, Salt and pepper and speckle noise. The noise density was gradually added to MRI image up to 90% to compare performance of the filters by qualitative and quantitative evaluation. The performance of these filters are compared using the statistical parameters such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The study shows that Median filter reconstructs a high quality image than other filters in Gaussian and Salt and pepper denoising with 38.3 dB PSNR at 10% noise variance. While for speckle noise removal, Average filter is perform better than others which result of 56.2 dB PSNR at 10% noise variance. A comparison with other well-established methods, this study shows that the Median and Average filter produces better denoising results, preserving the main structures and details. (C) 2015 The Authors. Published by Elsevier B.V.