  A computer-aided detection (CAD) system for the identification of lung
internal nodules in low-dose multi-detector helical Computed Tomography (CT)
images was developed in the framework of the MAGIC-5 project. The three modules
of our lung CAD system, a segmentation algorithm for lung internal region
identification, a multi-scale dot-enhancement filter for nodule candidate
selection and a multi-scale neural technique for false positive finding
reduction, are described. The results obtained on a dataset of low-dose and
thin-slice CT scans are shown in terms of free response receiver operating
characteristic (FROC) curves and discussed.
