A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

Donato Cascio, Delogu, Fulcheri, Tommasi, Gargano, Cheran, Grosso, Francesco De Carlo, Retico, Bruno Golosio, Cerello, De Carlo, De Mitri, Bellotti, Tangaro, Squarcia, Catanzariti

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79 Citations (Scopus)

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. © 2007 American Association of Physicists in Medicine.
Original languageEnglish
Pages (from-to)4901-4910
Number of pages10
JournalMedical Physics
Volume34
Publication statusPublished - 2007

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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