An ensemble classification-based approach applied to retinal blood vessel segmentation

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 9, 2012, Pages 2538-2548

  Fraz, Muhammad Moazam ^a   Remagnino, Paolo ^a   Hoppe, Andreas ^a   Uyyanonvara, Bunyarit ^b   Rudnicka, Alicja R ^c   Owen, Christopher G ^c   Barman, Sarah A ^a  

^a KINGSTON UNIVERSITY   (United Kingdom)

^b THAMMASAT UNIVERSITY   (Thailand)

^c ST GEORGE S HOSPITAL   (United Kingdom)

Author keywords

Ensemble classification; medical image analysis; retinal blood vessels; segmentation

Indexed keywords

BOOSTED DECISION TREES; DATA SETS; ENGLAND; ENSEMBLE CLASSIFICATION; ENSEMBLE SYSTEMS; FEATURE VECTORS; FILTER RESPONSE; GRADIENT VECTOR FIELD; HEALTH-STUDY; LINE STRENGTH; MEDICAL IMAGE ANALYSIS; MORPHOLOGICAL TRANSFORMATIONS; ORIENTATION ANALYSIS; PATHOLOGICAL RETINAL IMAGES; RETINAL BLOOD VESSELS; RETINAL IMAGE; RETINAL IMAGE ANALYSIS; RETINAL VESSELS;

BLOOD VESSELS; DECISION TREES; IMAGE ANALYSIS; IMAGE SEGMENTATION; PHOTOGRAPHY;

OPHTHALMOLOGY;

ALGORITHM; ANALYTICAL ERROR; ARTICLE; AUTOMATION; CLASSIFICATION; CONTROLLED STUDY; DATA BASE; EYE PHOTOGRAPHY; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; IMAGE ANALYSIS; INTERMETHOD COMPARISON; MORPHOLOGY; RETINA BLOOD VESSEL;

ALGORITHMS; AREA UNDER CURVE; CHILD; DATABASES, FACTUAL; DECISION TREES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; RETINAL VESSELS;

EID: 84865463567     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2205687     Document Type: Article

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