A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency. (c) 2007 American Association of Physicists in Medicine.
|Autori interni:||DELOGU, PASQUALE|
|Autori:||Bellotti R; De Carlo F; Gargano G; Tangaro S; Cascio D; Catanzariti E; Cerello P; Cheran SC; Delogu P; De Mitri I; Fulcheri C; Grosso D; Retico A; Squarcia S; Tommasi E; Golosio B|
|Titolo:||A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model|
|Anno del prodotto:||2007|
|Digital Object Identifier (DOI):||10.1118/1.2804720|
|Appare nelle tipologie:||1.1 Articolo in rivista|