title: A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.
abstract: 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.
authors.affiliation: Dipartimento di Fisica, Università di Bari, Italy.
authors.forename: R
authors.initials: R
authors.lastname: Bellotti
authors.forename: F
authors.initials: F
authors.lastname: De Carlo
authors.forename: G
authors.initials: G
authors.lastname: Gargano
authors.forename: S
authors.initials: S
authors.lastname: Tangaro
authors.forename: D
authors.initials: D
authors.lastname: Cascio
authors.forename: E
authors.initials: E
authors.lastname: Catanzariti
authors.forename: P
authors.initials: P
authors.lastname: Cerello
authors.forename: S C
authors.initials: SC
authors.lastname: Cheran
authors.forename: P
authors.initials: P
authors.lastname: Delogu
authors.forename: I
authors.initials: I
authors.lastname: De Mitri
authors.forename: C
authors.initials: C
authors.lastname: Fulcheri
authors.forename: D
authors.initials: D
authors.lastname: Grosso
authors.forename: A
authors.initials: A
authors.lastname: Retico
authors.forename: S
authors.initials: S
authors.lastname: Squarcia
authors.forename: E
authors.initials: E
authors.lastname: Tommasi
authors.forename: Bruno
authors.initials: B
authors.lastname: Golosio
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology*; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging; D008954:Models, Biological; D016571:Neural Networks, Computer; D012372:ROC Curve; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed