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A CAD system for nodule detection in low‐dose lung CTs based on region growing and a new active contour model
Author(s) -
Bellotti R.,
De Carlo F.,
Gargano G.,
Tangaro S.,
Cascio D.,
Catanzariti E.,
Cerello P.,
Cheran S. C.,
Delogu P.,
De Mitri I.,
Fulcheri C.,
Grosso D.,
Retico A.,
Squarcia S.,
Tommasi E.,
Golosio Bruno
Publication year - 2007
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2804720
Subject(s) - nodule (geology) , cad , false positive paradox , computer science , nuclear medicine , lung cancer , lung , radiology , artificial intelligence , medicine , pathology , paleontology , engineering drawing , engineering , biology
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.