HARDI based pattern classifiers for the identification of white matter pathologies

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

Volumn 6892 LNCS, Issue PART 2, 2011, Pages 234-241

  Bloy, Luke ^a   Ingalhalikar, Madhura ^a   Eavani, Harini ^a,b   Roberts, Timothy P L ^b   Schultz, Robert T ^b   Verma, Ragini ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CHILDREN S HOSPITAL OF PHILADELPHIA   (United States)

Author keywords

Classification; Diffusion Imaging; FOD; HARDI; SVM

Indexed keywords

AUTISM SPECTRUM DISORDERS; DIFFUSION IMAGING; DIFFUSION MODEL; DIMENSIONALITY REDUCTION; FEATURE VECTORS; FIBER ORIENTATION DISTRIBUTION; FOD; HARDI; HIGH ANGULAR RESOLUTION DIFFUSION IMAGING; INVARIANT FEATURES; LINEAR SUPPORT VECTOR MACHINES; PATTERN CLASSIFIER; PROBABILISTIC SCORES; REGISTRATION TECHNIQUES; SVM; WHITE MATTER;

CLASSIFICATION (OF INFORMATION); DIFFUSION; DISEASES; IMAGE RETRIEVAL; MEDICAL COMPUTING; MEDICAL IMAGING; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; AUTISM; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN MAPPING; CHILD; DIFFUSION TENSOR IMAGING; HUMAN; METHODOLOGY; MYELINATED NERVE; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; BRAIN; BRAIN MAPPING; CHILD; CHILD DEVELOPMENT DISORDERS, PERVASIVE; DIFFUSION TENSOR IMAGING; HUMANS; MODELS, STATISTICAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 80053514758     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23629-7_29     Document Type: Conference Paper

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