Year: 2007
Type: article
Abstract: A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
Source: Journal of Instrumentation
Institutions: Bracco (Italy), Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, University of Pisa, Centro Ricerche Enrico Fermi, University of Salento, Istituto Nazionale di Fisica Nucleare, Sezione di Lecce, Istituto Nazionale di Fisica Nucleare, Sezione di Genova, University of Genoa, Istituto Nazionale di Fisica Nucleare, Sezione di Torino, University of Turin, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, University of Bari Aldo Moro, University of Palermo, University of Naples Federico II, University of Sassari, Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: Università di Pisa, Bracco Imaging
OpenAlex ID: W2049966525