Graph cuts with invariant object-interaction priors: Application to intervertebral disc segmentation

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

Volumn 6801 LNCS, Issue , 2011, Pages 221-232

  Ben Ayed, Ismail ^a,b   Punithakumar, Kumaradevan ^a,b   Garvin, Gregory ^b   Romano, Walter ^b   Li, Shuo ^a,b  

^a GE HEALTHCARE   (Canada)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUXILIARY FUNCTIONS; FAST COMPUTATION; FIXED POINT EQUATION; GEOMETRIC ATTRIBUTES; GLOBAL MEASURES; GRAPH CUT; INTERVERTEBRAL DISCS; LUMBAR SPINES; MANUAL SEGMENTATION; NP-HARD PROBLEM; POSE ESTIMATION; PRIOR KNOWLEDGE; QUANTITATIVE EVALUATION; REAL-TIME SOLUTIONS; SCALE INVARIANCE; SHAPE DEFORMATION; TEMPLATE-BASED; TRAINING SETS;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA PROCESSING; GRAPHIC METHODS; IMPLANTS (SURGICAL); MAGNETIC RESONANCE; MEDICAL IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; INTERVERTEBRAL DISK; LUMBAR VERTEBRA; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INTERVERTEBRAL DISK; LUMBAR VERTEBRAE; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79959608351     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22092-0_19     Document Type: Conference Paper

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