Hierarchical discriminative framework for detecting tubular structures in 3D images

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

Volumn 7917 LNCS, Issue , 2013, Pages 328-339

  Breitenreicher, Dirk ^a   Sofka, Michal ^a   Britzen, Stefan ^b   Zhou, Shaohua K ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b Imaging and Therapy Systems Division   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRIMINATIVE CLASSIFIERS; IMAGING ARTIFACTS; INDICATOR FUNCTIONS; ROBUST MEASURES; SHORTEST PATH ALGORITHMS; STATE-OF-THE-ART APPROACH; SURGICAL PLANNING; TUBULAR STRUCTURES;

DATA PROCESSING;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DISCRIMINANT ANALYSIS; HUMAN; IMAGE QUALITY; LUNG; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; HUMANS; IMAGING, THREE-DIMENSIONAL; LUNG; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84901261356     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38868-2_28     Document Type: Conference Paper

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