An automated system for lung nodule detection in low-dose computed tomography

I. Gori; M. E. Fantacci; A. Preite Martinez; A. Retico

doi:10.1117/12.709642

30 March 2007 An automated system for lung nodule detection in low-dose computed tomography

I. Gori, M. E. Fantacci, A. Preite Martinez, A. Retico

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65143R (2007) https://doi.org/10.1117/12.709642
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was 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, a dot-enhancement filter for nodule candidate selection 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 results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

Citation Download Citation

I. Gori, M. E. Fantacci, A. Preite Martinez, and A. Retico "An automated system for lung nodule detection in low-dose computed tomography", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143R (30 March 2007); https://doi.org/10.1117/12.709642

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