Bayesian maximal paths for coronary artery segmentation from 3D CT angiograms

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

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 222-229

  Lesage, David ^a,b   Angelini, Elsa D ^b   Bloch, Isabelle ^b   Funka Lea, Gareth ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b INSTITUT TELECOM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM EVALUATION; BAYESIAN; BAYESIAN MODEL; CARDIAC CTA; CENTERLINES; COMPUTATIONAL PERFORMANCE; CORONARY ARTERIES; CORONARY ARTERY SEGMENTATION; D-SPACE; DESIGN ISSUES; GROUND TRUTH; HEURISTIC SCHEMES; HIGH POTENTIAL; INTERACTIVE IMPLEMENTATION; LOCAL DATA; MAXIMAL PATH; MINIMAL COST; MINIMAL PATH; MULTISCALES; OPTIMIZATION SCHEME; PRIOR KNOWLEDGE; PROBABILISTIC FORMULATION; ROTTERDAM; SCALE-SPACE; TWO-POINT;

ANEMOMETERS; BAYESIAN NETWORKS; BLOOD VESSELS; COMPUTATIONAL EFFICIENCY; COMPUTERIZED TOMOGRAPHY; DATA FLOW ANALYSIS; HEART; IMAGE QUALITY; MEDICAL COMPUTING; SPACE RESEARCH;

THREE DIMENSIONAL;

ALGORITHM; ANGIOCARDIOGRAPHY; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CORONARY ARTERY DISEASE; HUMAN; IMAGE QUALITY; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CORONARY ANGIOGRAPHY; CORONARY ARTERY DISEASE; HUMANS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 80455124513     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_28     Document Type: Conference Paper

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