Adaptive Riemannian metrics for improved geodesic tracking of white matter

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6801 LNCS, Issue , 2011, Pages 13-24

  Hao, Xiang ^a   Whitaker, Ross T ^a   Fletcher, P Thomas ^a  

^a UNIVERSITY OF UTAH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION TENSOR IMAGING; INVERSE DIFFUSION; LOWER COST; POISSON'S EQUATION; RIEMANNIAN MANIFOLD; RIEMANNIAN METRICS; SCALAR FIELDS; SHORTEST PATH; VARIATIONAL PROBLEMS; WHITE MATTER; WHITE MATTER TRACTS;

DATA PROCESSING; EIGENVALUES AND EIGENFUNCTIONS; GEODESY; GEOMETRY; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; POISSON EQUATION;

TENSORS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION TENSOR IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; ULTRASTRUCTURE;

ALGORITHMS; BRAIN; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 80052308126     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22092-0_2     Document Type: Conference Paper

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