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EnglishA computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan). (c) 2008 Elsevier Ltd. All rights reserved.
| Autori interni: | |
| Autori: | Retico A; Delogu P; Fantacci M; Gori I; Martinez AP |
| Titolo: | Lung nodule detection in low-dose and thin-slice computed tomography |
| Anno del prodotto: | 2008 |
| Digital Object Identifier (DOI): | 10.1016/j.compbiomed.2008.02.001 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
http://hdl.handle.net/11568/193688
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