NDLI: Using bilateral symmetry to improve non-local means denoising of MR brain images

Content Provider	IEEE Xplore Digital Library
Author	Prima, S. Commowick, O.
Copyright Year	2013
Description	Author affiliation: INSERM, INRIA, Rennes, France (Prima, S.; Commowick, O.)
Abstract	The popular NL-means denoising algorithm proposes to modify the intensity of each voxel of an image by a weighted sum of the intensities of similar voxels. The success of the NL-means rests on the fact that there are typically enough such similar voxels in natural, and even medical images; in other words, that there is some self-similarity/redundancy in such images. However, similarity between voxels (or rather, between patches around them) is usually only assessed in a spatial neighbourhood of the voxel under study. As the human brain exhibits approximate bilateral symmetry, one could wonder whether a voxel in a brain image could be more accurately denoised using information from both ipsi- and contralateral hemispheres. This is the idea we investigate in this paper. We define and compute a mid-sagittal plane which best superposes the brain with itself when mirrored about the plane. Then we use this plane to double the size of the neighbourhoods and hopefully find additional interesting voxels to be included in the weighted sum. We evaluate this strategy using an extensive set of experiments on both simulated and real datasets.
Sponsorship	IEEE
Starting Page	1231
Ending Page	1234
File Size	3084453
Page Count	4
File Format	PDF
ISBN	9781467364560
ISSN	19457928
e-ISBN	9781467364553
DOI	10.1109/ISBI.2013.6556703
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Brain Noise measurement PSNR Image resolution Magnetic resonance imaging NL-means MRI brain bilateral symmetry denoising
Content Type	Text
Resource Type	Article

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