A generalized learning based framework for fast brain image registration

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

Volumn 6362 LNCS, Issue PART 2, 2010, Pages 306-314

  Kim, Minjeong ^a   Wu, Guorong ^a   Yap, Pew Thian ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN IMAGES; COMPUTATION COSTS; DEFORMABLE REGISTRATION; DEFORMATION PREDICTION; IMAGE SAMPLES; LOCAL MINIMUMS; REGISTRATION ACCURACY; RESIDUAL DEFORMATION; SHAPE DEFORMATION; SUBJECT-SPECIFIC; SUPPORT VECTOR REGRESSIONS;

FORECASTING; HAMMERS; MEDICAL COMPUTING; MEDICAL IMAGING;

DEFORMATION;

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

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

EID: 84883840777     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15745-5_38     Document Type: Conference Paper

References (7)

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