Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features

IEEE Transactions on Medical Imaging

Volumn 35, Issue 1, 2016, Pages 144-157

  Chong, Daniel Y ^a   Kim, Hyun J ^b   Lo, Pechin ^a   Young, Stefano ^a   McNitt Gray, Michael F ^a   Abtin, Fereidoun ^a   Goldin, Jonathan G ^a   Brown, Matthew S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NONE

Author keywords

Fibrotic interstitial lung disease; High resolution computed tomography; Machine learning; Pattern recognition and classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; FEATURE EXTRACTION; LEARNING SYSTEMS; PATTERN RECOGNITION; PULMONARY DISEASES; STATISTICAL TESTS; SUPPORT VECTOR MACHINES; TOMOGRAPHY;

CLASSIFIER PERFORMANCE; COMPUTER AIDED DIAGNOSIS SYSTEMS; HIGH-RESOLUTION COMPUTED TOMOGRAPHY; INTERSTITIAL LUNG DISEASE; MULTI-CENTER CLINICAL TRIALS; PATTERN RECOGNITION AND CLASSIFICATION; RECONSTRUCTION KERNEL; SUPPORT VECTOR MACHINE CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

ADULT; ARTICLE; CLASSIFICATION ALGORITHM; COMPUTED TOMOGRAPHY SCANNER; COMPUTER PROGRAM; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; EXTENDED G MEAN; FIBROSING ALVEOLITIS; HIGH RESOLUTION COMPUTER TOMOGRAPHY; HUMAN; IMAGE ANALYSIS; KAPPA STATISTICS; LUNG BLOOD VESSEL; LUNG FIBROSIS; LUNG PARENCHYMA; MAJOR CLINICAL STUDY; ROBUSTNESS DRIVEN FEATURE SELECTION; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; AUTOMATED PATTERN RECOGNITION; DIAGNOSTIC IMAGING; IMAGE PROCESSING; INTERSTITIAL LUNG DISEASE; MACHINE LEARNING; PROCEDURES; X-RAY COMPUTED TOMOGRAPHY;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG DISEASES, INTERSTITIAL; MACHINE LEARNING; PATTERN RECOGNITION, AUTOMATED; PULMONARY FIBROSIS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84959318802     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2459064     Document Type: Article

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