Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

DOI10.1080/02664763.2015.1080671OpenAlexW2208314483WikidataQ58419202 ScholiaQ58419202MaRDI QIDQ5138052

Li Bai, Alexey Koloydenko, Koenraad M. R. Audenaert, Ian L. Dryden, Diwei Zhou

Publication date: 3 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://eprints.nottingham.ac.uk/41115/

zbMATH Keywords

smoothingmetricanisotropypositive definiteProcrustesRiemannianweighted Fréchet mean

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Image analysis in multivariate analysis (62H35)

Related Items

Concurrent object regression, Total variation regularized Fréchet regression for metric-space valued data

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Norm inequalities related to the matrix geometric mean
• Riemannian geometry for the statistical analysis of diffusion tensor data
• Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging
• Generalized Procrustes analysis
• Least angle regression. (With discussion)
• A Riemannian framework for tensor computing
• Distances and inference for covariance operators
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Computer Vision - ECCV 2004