http://ajer.org/papers/v5(02)/F0502042048.pdf4D Collaborative Non-Local Means Based Diffusion MRI Denoising
Journal Title: American journal of Engineering Research - Year 2016, Vol 5, Issue 2
Abstract
Noise is a major issue that reduces the quality of images acquired by diffusion MRI (dMRI). Recently, the non-local means (NLM) algorithm has been proposed and successfully applied in dMRI denoising. However, NLM relies on self-similarity information and tends to fails when recurrent image structures cannot be located. To address this issue, we introduce the improved collaborative NLM. Both inner-image and interimage similarity information are used. Specifically, a group of co-denoising images are first registered to the target space. NLM-like block matching is then performed on both target noisy image and co-denoising images. This formulation can significantly increase the amount of similarity information and reduce the rare patch effect. Moreover, in order to adapt to the characteristics of dMRI, we present a complete denoising framework with multiple techniques including 4D image block, pseudo-residual-based noise standard deviation estimation, Rician bias correction, and block preselection. Extensive experiments on both synthetic and real data demonstrate that the proposed framework outperforms the classical NLM method. Keywords: Block Matching, Denoising, Diffusion MRI, Non-Local Means, Kernel Regression
Authors and Affiliations
Geng Chen,, Yafeng Wu*
Keywords
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