Multivariate high-dimensional cortical folding analysis, combining complexity and shape, in neonates with congenital heart disease

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

Volumn 5636 LNCS, Issue , 2009, Pages 552-563

  Awate, Suyash P ^a   Yushkevich, Paul ^a   Song, Zhuang ^a   Licht, Daniel ^b   Gee, James C ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CHILDREN S HOSPITAL OF PHILADELPHIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CEREBRAL CORTEX; CLINICAL DATA; CONGENITAL HEART DISEASE; CORTICAL FOLDING; CORTICAL THICKNESS; DESCRIPTORS; FOLDING PATTERNS; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL; NON-PARAMETRIC PERMUTATION; NOVEL APPLICATIONS; REGION OF INTEREST; STATISTICAL FRAMEWORK; STATISTICAL HYPOTHESIS TESTING;

CARDIOLOGY; DATA PROCESSING; RELIABILITY ANALYSIS; TESTING;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN CORTEX; COMPUTER ASSISTED DIAGNOSIS; CONGENITAL HEART MALFORMATION; CONGENITAL MALFORMATION; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MULTIVARIATE ANALYSIS; NEWBORN; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBRAL CORTEX; HEART DEFECTS, CONGENITAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFANT, NEWBORN; MAGNETIC RESONANCE IMAGING; MULTIVARIATE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 70349318362     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02498-6_46     Document Type: Conference Paper

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