NDLI: Collaborative non-local means denoising of magnetic resonance images

Content Provider	IEEE Xplore Digital Library
Author	Geng Chen Pei Zhang Yafeng Wu Dinggang Shen Pew-Thian Yap
Copyright Year	2015
Description	Author affiliation: Dept. of Radiol., UNC Chapel Hill, Chapel Hill, NC, USA (Pei Zhang; Dinggang Shen; Pew-Thian Yap) \|\| Data Process. Center, Northwestern Polytech. Univ., Xi'an, China (Geng Chen; Yafeng Wu)
Abstract	Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. To reduce noise, the non-local means (NLM) filter has been shown to yield state-of-the-art denoising performance. However, NLM relies heavily on the existence of recurring structural patterns and this condition might not always be satisfied especially within a single image, where complex patterns might not recur. In this paper, we propose to leverage common structures from multiple images to collaboratively denoise an image. The assumption is that, although the human brain is structurally complex, common structures can be found with greater probability from multiple scans than from a single scan. More specifically, to denoise an image, multiple images from different individuals are spatially aligned to the image and NLM-like block matching is performed on these aligned images with the image as the reference. Experiments on synthetic and real data indicate that the proposed approach - collaborative non-local means (CNLM) - outperforms the classic NLM and yields results with markedly improved structural details.
Starting Page	564
Ending Page	567
File Size	1165172
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163936
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Noise Noise measurement Rician channels Collaboration Magnetic resonance Image edge detection patch-based approach MRI denoising non-local means filter edge-preserving denoising
Content Type	Text
Resource Type	Article

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