Parameterization-invariant shape statistics and probabilistic classification of anatomical surfaces

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

Volumn 6801 LNCS, Issue , 2011, Pages 147-158

  Kurtek, Sebastian ^a   Klassen, Eric ^a   Ding, Zhaohua ^b   Avison, Malcolm J ^b   Srivastava, Anuj ^a  

^a FLORIDA STATE UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

ADHD; anatomical structures; classification; parameterization invariance; Riemannian framework; shape statistics and models

Indexed keywords

ADHD; ANATOMICAL STRUCTURES; ATTENTION DEFICIT HYPERACTIVITY DISORDER; BRAIN STRUCTURE; CLASSIFICATION PERFORMANCE; CLASSIFICATION RATES; CLASSIFICATION RESULTS; COVARIANCE STRUCTURES; GEOMETRIC FEATURE; PARAMETERIZED; PROBABILISTIC CLASSIFICATION; PROBABILITY MODELS; RANDOM SAMPLING; RIEMANNIAN FRAMEWORK; RIEMANNIAN METRICS; SHAPE MODEL; SHAPE STATISTICS;

DATA PROCESSING; DISEASE CONTROL; MEDICAL IMAGING; PARAMETERIZATION; SAMPLING;

THREE DIMENSIONAL;

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

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ATTENTION DEFICIT DISORDER WITH HYPERACTIVITY; BRAIN; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 80052328274     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22092-0_13     Document Type: Conference Paper

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