A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.
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.
Bellotti
R
R
Dipartimento di Fisica, Università di Bari, Italy.
De Carlo
F
F
Gargano
G
G
Tangaro
S
S
Cascio
D
D
Catanzariti
E
E
Cerello
P
P
Cheran
S C
SC
Delogu
P
P
De Mitri
I
I
Fulcheri
C
C
Grosso
D
D
Retico
A
A
Squarcia
S
S
Tommasi
E
E
Golosio
Bruno
B
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