A new computationally efficient CAD system for pulmonary nodule detection in CT imagery

Medical Image Analysis

Volumn 14, Issue 3, 2010, Pages 390-406

  Messay, Temesguen ^a   Hardie, Russell C ^a   Rogers, Steven K ^b  

^a University of Dayton   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

ANODE09; Computed tomography (CT); Computer aided detection (CAD); LIDC; Lung nodule

Indexed keywords

CAD SYSTEM; CLINICAL MANAGEMENT; COMPUTATIONALLY EFFICIENT; COMPUTED TOMOGRAPHY; COMPUTER AIDED DETECTION SYSTEMS; COMPUTER-AIDED DETECTION; CROSS VALIDATION; CT SCAN; DATA SETS; EARLY DETECTION; EFFECTIVE SIZE; FALSE POSITIVE; FISHER LINEAR DISCRIMINANTS; GROUND TRUTH; IMAGE DATABASE; LUNG CANCER; LUNG NODULE; LUNG REGIONS; LUNG SEGMENTATION; MORPHOLOGICAL PROCESSING; PERFORMANCE ANALYSIS; PERFORMANCE COMPARISON; PULMONARY NODULE DETECTION; PULMONARY NODULES; RESEARCH EFFORTS; SEGMENTATION ALGORITHMS; SEQUENTIAL FORWARD SELECTION; THRESHOLDING; UNIVERSITY OF TEXAS MEDICAL BRANCHES;

BIOLOGICAL ORGANS; CLASSIFIERS; DATABASE SYSTEMS; FEATURE EXTRACTION; POSITRON EMISSION TOMOGRAPHY; STATISTICAL TESTS;

COMPUTERIZED TOMOGRAPHY;

ACCURACY; ALGORITHM; ARTICLE; AUTOMATION; CLASSIFIER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; FALSE POSITIVE RESULT; INTERMETHOD COMPARISON; LUNG NODULE; PRIORITY JOURNAL; QUALITY CONTROL; SENSITIVITY AND SPECIFICITY; TUMOR VOLUME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LUNG NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 77951647196     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2010.02.004     Document Type: Article

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