Efficient large deformation registration via geodesics on a learned manifold of images

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

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 680-687

  Hamm, Jihun ^a   Davatzikos, Christos ^a   Verma, Ragini ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

2D IMAGES; 3D MOUSE; BRAIN VOLUME; EFFICIENT METHOD; GEODESIC DISTANCES; GRAPH REPRESENTATION; LARGE DEFORMATIONS; MANIFOLD LEARNING; OPTIMAL PATHS; REGISTRATION METHODS; REGISTRATION PROBLEMS; SHORTEST PATH;

DEFORMATION; GEODESY; LEARNING ALGORITHMS; MEDICAL COMPUTING; OPTIMIZATION;

THREE DIMENSIONAL;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; MOUSE; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BRAIN; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MICE; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 79551685631     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_84     Document Type: Conference Paper

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