A computationally efficient non-local maximum likelihood estimation approach for Rician noise reduction in MRI

Abstract

Magnetic resonance images are subjected to degradation due to the presence of noise from various sources. For further processing of the MR images and for effective visual analysis, the noise should be removed. Many denoising algorithms have been proposed for enhancing MR images. Preserving the structural details has its own importance in image denoising, and especially for medical images it should not be compromised. For effective image denoising, the data distribution in the images should be known in advance. Data in the magnitude MR images are Rician distributed (if acquired with single coil). Among the recently proposed denoising methods for reducing Rician noise, non-local maximum likelihood estimation method (NLML) proved to be an efficient one. But the superior performance of the NLML is restricted by its high computational complexity and non optimal way of selecting the samples for ML estimation. In this paper we address the above problems to some extend by introducing rotation invariant similarity measures. The proposed method aims to reduce the computational complexity and preserves the edges by discarding the dissimilar pixels by using a two-level refining approach. Comparative analysis of the proposed method with conventional NLML method based on the execution time and quantitative analysis in terms of PSNR, SSIM, FSIM and UIQI shows that the proposed method has an edge over the conventional method.