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 (~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
@article{arxiv.0707.2696,
title = {Automated detection of lung nodules in low-dose computed tomography},
author = {D. Cascio and S. C. Cheran and A. Chincarini and G. De Nunzio and P. Delogu and M. E. Fantacci and G. Gargano and I. Gori and G. L. Masala and A. Preite Martinez and A. Retico and M. Santoro and C. Spinelli and T. Tarantino},
journal= {arXiv preprint arXiv:0707.2696},
year = {2007}
}
Comments
4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-359