A log-euclidean framework for statistics on diffeomorphisms

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

Volumn 4190 LNCS - I, Issue , 2006, Pages 924-931

  Arsigny, Vincent ^a   Commowick, Olivier ^a,b   Pennec, Xavier ^a   Ayache, Nicholas ^a  

^a INRIA   (France)

^b DOSIsoft SA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; COMPUTATIONAL METHODS; CONSTRAINT THEORY; DATABASE SYSTEMS; MAGNETIC RESONANCE IMAGING; STATISTICAL METHODS; VECTORS;

ATLAS; DIFFEOMORPHISM; DISPLACEMENT FIELDS; VECTOR FIELDS;

DEFORMATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84883831948     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11866565_113     Document Type: Conference Paper

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