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

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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

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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.

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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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