Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Variational Frameworks for DT-MRI Estimation, Regularization and Visualization
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Rician noise removal in diffusion tensor MRI
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast and simple calculus on tensors in the log-euclidean framework
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast non local means denoising for 3d MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Impact of Rician Adapted Non-Local Means Filtering on HARDI
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Design and Construction of a Realistic DWI Phantom for Filtering Performance Assessment
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Non-local adaptive structure tensors
Image and Vision Computing
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
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Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed in this paper are based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. We introduce three variations of the NL-means algorithms adapted to DW-MRI and to DT-MRI. Experiments were carried out on a set of 12 diffusion-weighted images (DW-MRI) of the same subject. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.