Robust Brain MRI Denoising and Segmentation Using Enhanced non-local Means Algorithm

Image denoising is an integral component of many practical medical systems. Non-local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non-local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non-local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c-means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM-filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1-weighted (T1-w) and T2-weighted (T2-w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2-weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques. Copyright © 2014 Wiley Periodicals, Inc.