P
Pierrick Coupé
Researcher at L'Abri
Publications - 201
Citations - 11121
Pierrick Coupé is an academic researcher from L'Abri. The author has contributed to research in topics: Segmentation & Noise reduction. The author has an hindex of 45, co-authored 183 publications receiving 9147 citations. Previous affiliations of Pierrick Coupé include University of Bordeaux & Polytechnic University of Valencia.
Papers
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Journal ArticleDOI
An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images
TL;DR: The results show that the optimized NL-means filter outperforms the classical implementation of the NL- means filter, as well as two other classical denoising methods and total variation minimization process in terms of accuracy with low computation time.
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Adaptive non‐local means denoising of MR images with spatially varying noise levels
TL;DR: In this article, the authors proposed a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter, which is automatically obtained from the images using a new local noise estimation method.
Journal ArticleDOI
Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.
Pierrick Coupé,José V. Manjón,Vladimir S. Fonov,Jens C. Pruessner,Montserrat Robles,D. Louis Collins +5 more
TL;DR: Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation in quantitative magnetic resonance analysis.
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Nonlocal Means-Based Speckle Filtering for Ultrasound Images
TL;DR: Results on real images demonstrate that the proposed adaptation of the nonlocal (NL)-means filter for speckle reduction in ultrasound (US) images is able to preserve accurately edges and structural details of the image.
Journal ArticleDOI
BEaST: brain extraction based on nonlocal segmentation technique.
Simon Fristed Eskildsen,Pierrick Coupé,Vladimir S. Fonov,José V. Manjón,Kelvin K. Leung,Nicolas Guizard,Shafik N. Wassef,Lasse Riis Østergaard,D. Louis Collins +8 more
TL;DR: A new robust method dedicated to produce consistent and accurate brain extraction based on nonlocal segmentation embedded in a multi-resolution framework, which provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.