DTI segmentation and fiber tracking using metrics on multivariate normal distributions

DOI10.1007/S10851-013-0466-ZzbMath1365.68452OpenAlexW2088141002MaRDI QIDQ2251274

Minyeon Han, Frank Chongwoo Park

Publication date: 11 July 2014

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-013-0466-z

zbMATH Keywords

Riemannian geometrymagnetic resonance imagingimage segmentationmultivariate normal distributiondiffusion tensorfiber tracking

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Measures of information, entropy (94A17) Statistical aspects of information-theoretic topics (62B10)

Cites Work

• Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing
• Riemannian geometry for the statistical analysis of diffusion tensor data
• Multivariate normal distributions parametrized as a Riemannian symmetric space
• A Riemannian framework for tensor computing
• A distance between multivariate normal distributions based in an embedding into the Siegel group
• Multivalued Geodesic Ray-Tracing for Computing Brain Connections Using Diffusion Tensor Imaging
• 10.1162/153244304322765649
• A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices
• Covariance, subspace, and intrinsic Crame/spl acute/r-Rao bounds
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