Abstract 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 (similar to 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.
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|Titolo:||Automated detection of lung nodules in low-dose computed tomography|
|Rivista:||INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY|
|Citazione:||Cascio, D., Cheran, S.C., Chincarini, A., De Nunzio, G., Delogu, P., Fantacci, M., et al. (2007). Automated detection of lung nodules in low-dose computed tomography. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2(1), S357-S358.|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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