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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

Bellotti, Roberto and De Carlo, Francesco and Gargano, Gianfranco and Tangaro, Sonia and Cascio, Donato and Catanzariti, Ezio and Cerello, Piergiorgio and Cheran, Sorin Cristian and Delogu, Pasquale and De Mitri, Ivan and Fulcheri, C. and Grosso, Daniele and Retico, Alessandra and Squarcia, Sandro and Tommasi, E. and Golosio, Bruno (2007) A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Medical Physics, Vol. 34 (12), p. 4901-4910. eISSN 0094-2405. Article.

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DOI: 10.1118/1.2804720

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.

Item Type:Article
ID Code:2417
Status:Published
Refereed:Yes
Uncontrolled Keywords:Biomedical measurement, cancer, computerised tomography, edge detection, image segmentation, iterative methods, lung, medical image processing, neural nets, tumours
Subjects:Area 02 - Scienze fisiche > FIS/07 Fisica applicata (a beni culturali, ambientali, biologia e medicina)
Divisions:001 Università di Sassari > 03 Istituti > Matematica e fisica
Publisher:American Association of Physicists in Medicine
eISSN:0094-2405
Deposited On:18 Aug 2009 10:07

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