Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models

Computers in Biology and Medicine

Volumn 42, Issue 11, 2012, Pages 1098-1109

  Cascio, D ^a   Magro, R ^a   Fauci, F ^a   Iacomi, M ^a,b   Raso, G ^a  

^a UNIVERSITY OF PALERMO   (Italy)

^b Institutul de Ştiinţe Spaţiale ^*  (Romania)

Author keywords

Active contours models; Computer tomography (CT); Image features; Lung nodules; Mass spring models; Segmentation; Spline curves

Indexed keywords

ACTIVE CONTOURS MODEL; COMPUTER TOMOGRAPHY; IMAGE FEATURES; LUNG NODULE; MASS-SPRING MODELS; SPLINE CURVE;

BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; IMAGE SEGMENTATION; POSITRON EMISSION TOMOGRAPHY; THREE DIMENSIONAL; THREE DIMENSIONAL COMPUTER GRAPHICS;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; COMPUTER AIDED DESIGN; DATA BASE; DIAGNOSTIC ACCURACY; IMAGE PROCESSING; IMAGE RECONSTRUCTION; LUNG NODULE; LUNG PARENCHYMA; NERVE CELL NETWORK; PRIORITY JOURNAL; RADIOLOGIST; SPIRAL COMPUTER ASSISTED TOMOGRAPHY; STABLE THREE DIMENSIONAL MASS SPRING MODEL;

ALGORITHMS; DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGING, THREE-DIMENSIONAL; LUNG NEOPLASMS; MODELS, BIOLOGICAL; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, SPIRAL COMPUTED;

EID: 84867731465     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2012.09.002     Document Type: Article

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