FABC: Retinal vessel segmentation using AdaBoost

IEEE Transactions on Information Technology in Biomedicine

Volumn 14, Issue 5, 2010, Pages 1267-1274

  Lupaşcu, Carmen Alina ^a   Tegolo, Domenico ^a   Trucco, Emanuele ^b  

^a UNIVERSITY OF PALERMO   (Italy)

^b UNIVERSITY OF DUNDEE   (United Kingdom)

Author keywords

AdaBoost classifier; retinal images; vessel segmentation

Indexed keywords

ADABOOST; ADABOOST CLASSIFIER; ENCODING INFORMATIONS; FEATURE VECTORS; FEATURE-BASED; FIELD OF VIEWS; GOLD STANDARDS; IN-LINE; INTENSITY STRUCTURE; LABELED IMAGES; MANUAL SEGMENTATION; MULTIPLE SCALE; RECEIVER OPERATING CHARACTERISTIC CURVES; RETINAL IMAGE; RETINAL VESSELS; SPATIAL PROPERTIES; STATE-OF-THE-ART APPROACH; TEST SETS; TRAINING SETS; VESSEL EXTRACTION; VESSEL SEGMENTATION;

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; OPHTHALMOLOGY; PIXELS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGICAL MODEL; FACTUAL DATABASE; FLUORESCENCE ANGIOGRAPHY; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY; NORMAL DISTRIBUTION; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; RETINA BLOOD VESSEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES, FACTUAL; FLUORESCEIN ANGIOGRAPHY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; NORMAL DISTRIBUTION; REPRODUCIBILITY OF RESULTS; RETINAL VESSELS; ROC CURVE;

EID: 77956379265     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2010.2052282     Document Type: Article

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