Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

Computers in Biology and Medicine

Volumn 43, Issue 4, 2013, Pages 287-300

  Keshani, Mohsen ^a   Azimifar, Zohreh ^a   Tajeripour, Farshad ^a   Boostani, Reza ^a  

^a SHIRAZ UNIVERSITY   (Iran)

Author keywords

Active contour; Connectivity recognition; Detection; Lung nodules; Segmentation; SVM classifier

Indexed keywords

ACTIVE CONTOUR MODELING; ACTIVE CONTOURS; ANATOMICAL FEATURES; CONNECTIVITY RECOGNITION; LUNG NODULE; LUNG NODULE DETECTION; LUNG NODULE SEGMENTATION; SVM CLASSIFIERS;

BIOLOGICAL ORGANS; CONTOUR MEASUREMENT; ERROR DETECTION; IMAGE RETRIEVAL; IMAGE SEGMENTATION; INTELLIGENT SYSTEMS; SUPPORT VECTOR MACHINES; TISSUE;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; BRONCHIOLE; CLASSIFIER; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC ACCURACY; HISTOGRAM; HUMAN; HUMAN TISSUE; IMAGE QUALITY; K NEAREST NEIGHBOR; LUNG NODULE; LUNG PARENCHYMA; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; THICKNESS; THORAX WALL;

ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; EARLY DETECTION OF CANCER; HUMANS; LUNG; MODELS, THEORETICAL; PREDICTIVE VALUE OF TESTS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SOLITARY PULMONARY NODULE; STOCHASTIC PROCESSES; SUPPORT VECTOR MACHINES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84875375417     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2012.12.004     Document Type: Article

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