Robust large scale prone-supine polyp matching using local features: A metric learning approach

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

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 75-82

  Liu, Meizhu ^a,b   Lu, Le ^a,b   Bi, Jinbo ^a,b   Raykar, Vikas ^a,b   Wolf, Matthias ^a,b   Salganicoff, Marcos ^a,b  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION FEATURES; COLON SEGMENTATION; COLONIC POLYPS; COMPUTER AIDED DETECTION SYSTEMS; CT COLONOGRAPHY; DATA SETS; EARLY DETECTION; EFFECTIVE TOOL; GEOMETRIC INFORMATION; GLOBAL GEOMETRIES; LOCAL FEATURE; MATCHING METHODS; METRIC DISTANCES; METRIC LEARNING; MULTI-SITE; PAIRWISE MATCHING; PATIENT CASE; POLYP DETECTION;

COMPUTER AIDED DESIGN; COMPUTER AIDED INSTRUCTION; DISTANCE EDUCATION; FEATURE EXTRACTION; MEDICAL COMPUTING; MEDICAL IMAGING; STATISTICAL TESTS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BODY POSTURE; COLON POLYP; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; METHODOLOGY; RADIOGRAPHY; STATISTICAL MODEL; SUPINE POSITION;

ALGORITHMS; COLONIC POLYPS; DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRONE POSITION; SUPINE POSITION; TOMOGRAPHY, X-RAY COMPUTED;

EID: 82255183342     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_10     Document Type: Conference Paper

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