An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images

Coupé, Pierrick; Yger, Pierre; Prima, Sylvain; Hellier, Pierre; Kervrann, Charles; Barillot, Christian

doi:10.1109/tmi.2007.906087

Cited by 1,064 publications

(889 citation statements)

References 62 publications

Supporting

Mentioning

856

Contrasting

Unclassified

Order By: Relevance

“…In the current study, diffusion‐weighted MRI scans were converted from the native DICOM to compressed NIfTI‐1 format using the dcm2nii tool (http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html). Subsequently, the images were submitted to the DiPy v0.8.0 implementation (Garyfallidis et al., 2014) of a non‐local means de‐noising algorithm (Coupe et al., 2008) to boost the signal‐to‐noise ratio. Next, a brain mask of the b0 image was created using the brain extraction tool (BET) of the FMRIB Software Library (FSL) v5.0.8.…”

Section: Methodsmentioning

confidence: 99%

Differences in brain morphology and working memory capacity across childhood

Bathelt

Gathercole

Johnson

et al. 2017

Developmental Science

View full text Add to dashboard Cite

Working memory (WM) skills are closely associated with learning progress in key areas such as reading and mathematics across childhood. As yet, however, little is known about how the brain systems underpinning WM develop over this critical developmental period. The current study investigated whether and how structural brain correlates of components of the working memory system change over development. Verbal and visuospatial short‐term and working memory were assessed in 153 children between 5.58 and 15.92 years, and latent components of the working memory system were derived. Fractional anisotropy and cortical thickness maps were derived from T1‐weighted and diffusion‐weighted MRI and processed using eigenanatomy decomposition. There was a greater involvement of the corpus callosum and posterior temporal white matter in younger children for performance associated with the executive part of the working memory system. For older children, this was more closely linked with the thickness of the occipitotemporal cortex. These findings suggest that increasing specialization leads to shifts in the contribution of neural substrates over childhood, moving from an early dependence on a distributed system supported by long‐range connections to later reliance on specialized local circuitry. Our findings demonstrate that despite the component factor structure being stable across childhood, the underlying brain systems supporting working memory change. Taking the age of the child into account, and not just their overall score, is likely to be critical for understanding the nature of the limitations on their working memory capacity.

show abstract

Section: Methodsmentioning

confidence: 99%

Differences in brain morphology and working memory capacity across childhood

Bathelt

Gathercole

Johnson

et al. 2017

Developmental Science

View full text Add to dashboard Cite

show abstract

“…• Denoising All images in the database were first denoised with the 3D block-wise nonlocal means filter recently proposed for MR images by Coupe et al (2008). To remove the intensity bias introduced by the Rician nature of noise, a Rician adaptation of nonlocal means (WiestDaessle et al, 2008) was also used.…”

Section: Image Preprocessing For Library Constructionmentioning

confidence: 99%

“…For computational reasons, the entire image cannot be used and the number of pixels involved has to be reduced. As done for denoising (Buades et al, 2005;Coupe et al, 2008), we use a limited search volume V i , defined as a cube centered on the voxel x i under study. Thus, within each of the N selected subjects, we search for similar patches in a cubic region around the location under study (see Fig.…”

Section: Fig 2 Initialization Masksmentioning

confidence: 99%

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

et al. 2011

View full text Add to dashboard Cite

“…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

View full text Add to dashboard Cite

-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...

show abstract

An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images

Abstract: Abstract-

Cited by 1,064 publications

References 62 publications

Differences in brain morphology and working memory capacity across childhood

Differences in brain morphology and working memory capacity across childhood

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

BEaST: Brain extraction based on nonlocal segmentation technique

Contact Info

Product

Resources

About