Automated detection of lung nodules in low-dose computed tomography

Donato Cascio, Mario Santoro, Delogu, Preite Martinez, Spinelli, Santoro, Gargano, Chincarini, Cheran, Tarantino, Gori, Retico, De Nunzio, Fantacci, Masala, Teresa Tarantino

Research output: Contribution to conferenceOtherpeer-review

7 Citations (Scopus)


A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lungCAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (∼300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scan.
Original languageEnglish
Number of pages22
Publication statusPublished - 2007

All Science Journal Classification (ASJC) codes

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Automated detection of lung nodules in low-dose computed tomography'. Together they form a unique fingerprint.

Cite this