Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme

IEEE Journal of Biomedical and Health Informatics

Volumn 19, Issue 2, 2015, Pages 648-659

  Han, Hao ^a   Li, Lihong ^b   Han, Fangfang ^c   Song, Bowen ^a   Moore, William ^a   Liang, Zhengrong ^a  

^a STONY BROOK UNIVERSITY   (United States)

^b CITY UNIVERSITY OF NEW YORK   (United States)

^c NORTHEASTERN UNIVERSITY   (China)

Author keywords

computed tomography (CT) imaging; Computer aided detection (CADe); false positive (FP) reduction; lung nodules; vector quantization (VQ)

Indexed keywords

BIOLOGICAL ORGANS; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION; VECTORS;

ADAPTIVE DETECTION; COMPUTED TOMOGRAPHY SCAN; COMPUTER AIDED DETECTION; FALSE POSITIVE; FILTERING OPERATIONS; LUNG NODULE; PULMONARY NODULES; SUPPORT VECTOR MACHINE CLASSIFIERS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; FACTUAL DATABASE; HUMAN; LUNG; LUNG NODULE; LUNG TUMOR; PATHOLOGY; PROCEDURES; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; DATABASES, FACTUAL; HUMANS; LUNG; LUNG NEOPLASMS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84924738641     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2014.2328870     Document Type: Article

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