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 lung-CAD 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/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology
and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2,
Supplement 1, June 2007, pp 357-35