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 lungCAD 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/scan.
Lingua originale | English |
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Pagine | 351-372 |
Numero di pagine | 22 |
Stato di pubblicazione | Published - 2007 |
All Science Journal Classification (ASJC) codes
- ???subjectarea.asjc.2700.2746???
- ???subjectarea.asjc.2200.2204???
- ???subjectarea.asjc.2700.2741???
- ???subjectarea.asjc.1700.1707???
- ???subjectarea.asjc.1700.1706???
- ???subjectarea.asjc.2700.2718???
- ???subjectarea.asjc.1700.1704???