  A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
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, a dot-enhancement
filter for nodule candidate selection 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 results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.
