MRI denoising using Non-Local Means

Manjón, José V.; Carbonell-Caballero, José; Lull, Juan José; García-Martí, Gracián; Martí‐Bonmatí, Luis; Robles, Montserrat

doi:10.1016/j.media.2008.02.004

Cited by 510 publications

(393 citation statements)

References 17 publications

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“…The asymmetry of the Rician distribution results in a non-constant intensity bias as it depends on the local SNR. To reduce such bias, some authors have proposed to remove the bias in the squared magnitude image (Wiest-Daesslé et al, 2008, Manjón et al, 2008.…”

Section: Adaptation To Rician Noisementioning

confidence: 99%

“…This method reduces the noise by exploiting the self-similarity of the image patterns by averaging similar image patterns. In MRI, early works using the NLM method are from Coupe et al (2008) and Manjón et al (2008). The bibliography related to this method is quite extensive (Tristan-Vega et al, 20012;Coupe et al, 2012;Manjón et al, 2009Manjón et al, , 2010Manjón et al, , 2012Wiest-Daesslé et al, 2008;He and Greenshields, 2009;Rajan et al 2012Rajan et al , 2014.…”

Section: Introductionmentioning

confidence: 99%

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MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

157

122

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This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.3

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Section: Adaptation To Rician Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

157

122

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“…Regarding denoising, which is the other focus of this paper, groups in the DWI denoising community have first denoised DW data assuming a Gaussian noise on each separate DWI channel (Manjòn et al, 2008). These techniques were then extended to take into account the Rician noise nature of the DWI signal (Coupé et al, 2010;Descoteaux et al, 2008;Aja-Fernàndez et al, 2008;Tristán-Vega and Aja-Fernández, 2010;Aja-Fernández et al, 2011;Brion et al, 2011) and, recently, the non-central Chi-squared distribution in the case where parallel imaging is used Brion et al, 2011).…”

Section: Theorymentioning

confidence: 99%

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Gramfort¹,

Poupon²,

Descoteaux³

2014

Medical Image Analysis

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To cite this version:Alexandre Gramfort, Cyril Poupon, Maxime Descoteaux. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning. Medical Image Analysis, Elsevier, 2013, 18 (1) Abstract Diffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-ofthe-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI.

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“…A recent framework inspired by nonlocal means MRI denoising (Buades et al, 2005;Coupe et al, 2008;Manjon et al, 2008) has been introduced to achieve the label fusion segmentation task.…”

Section: Introductionmentioning

confidence: 99%

BEaST: Brain extraction based on nonlocal segmentation technique

et al. 2012

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-Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases.In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.Keywords: Brain extraction, skull stripping, patch-based segmentation, multi-resolution, MRI, BET ** Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. IntroductionBrain extraction (or skull stripping) is an important step in many neuroimaging analyses, such as registration, tissue classification, and segmentation. While methods such as the estimation of intensity normalization fields and registration do not require perfect brain masks, other methods such as measuring cortical thickness rely on very accurate brain extraction to work properly. For instance, failure to remove the dura may lead to an overestimation of cortical thickness (van der Kouwe et al., 2008), while removing part of the brain would lead to an underestimation. In cases of incorrect brain extraction, subjects may be excluded from further processing, a potentially expensive consequence for many studies. The solution of manually correcting the brain masks is a labour intensive and time-consuming task that is highly sensitive to inter-and intra-rater variability (Warfield et al., 2004).An accurate brain extraction method should exclude all tissues external to the brain, such as skull, dura, and eyes, without removing any part of the brain. The number of methods proposed to address the brain segmentation problem reflects the importance of accurate and robust brain extraction. During th...

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MRI denoising using Non-Local Means

Cited by 510 publications

References 17 publications

MRI noise estimation and denoising using non-local PCA

MRI noise estimation and denoising using non-local PCA

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

BEaST: Brain extraction based on nonlocal segmentation technique

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