Nonlinear embedding towards articulated spine shape inference using higher-order MRFs

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

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 579-586

  Kadoury, Samuel ^a   Paragios, Nikos ^b  

^a PHILIPS RESEARCH LABORATORIES   (Netherlands)

^b INRIA SACLAY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

HIGHER ORDER; IMAGE SUPPORT; LOW-DIMENSIONAL MANIFOLD EMBEDDING; MARKOV RANDOM FIELD; MESH MODEL; MODEL PARAMETERS; NONLINEAR EMBEDDING; SHAPE INFERENCE; SHAPE PARAMETERS; SHAPE VARIATIONS; SPATIAL DOMAINS; SPINAL COLUMN; STATISTICAL DISTRIBUTION; TRAINING SETS;

COMPUTERIZED TOMOGRAPHY; MEDICAL COMPUTING; OPTIMIZATION;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; METHODOLOGY; NONLINEAR SYSTEM; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPINE; SPINE DISEASE; STATISTICAL ANALYSIS;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; HUMANS; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPINAL DISEASES; SPINE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 79958772456     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_72     Document Type: Conference Paper

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