Geodesic shape regression in the framework of currents

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

Volumn 7917 LNCS, Issue , 2013, Pages 718-729

  Fishbaugh, James ^a   Prastawa, Marcel ^a   Gerig, Guido ^a   Durrleman, Stanley ^b  

^a UNIVERSITY OF UTAH   (United States)

^b PITIÉ SALPÊTRIÈRE HOSPITAL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; GEODESY; IMAGE CODING; MEDICAL IMAGING; REGRESSION ANALYSIS;

DEFORMATION PARAMETER; DIFFEOMORPHIC TRANSFORMATION; GENERATIVE MODEL; GRADIENT DESCENT ALGORITHMS; LARGE DEFORMATION DIFFEOMORPHIC METRIC MAPPINGS; OPTIMIZATION SCHEME; SHAPE REPRESENTATION; SIMPLE LINEAR REGRESSION;

DEFORMATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN NEOPLASMS; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; METHODS; PATHOLOGY; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84879876230     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38868-2_60     Document Type: Conference Paper

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