Automated detection of lung nodules in low-dose computed tomography

Donato Cascio, Mario Santoro, Delogu, Preite Martinez, Spinelli, Santoro, Gargano, Chincarini, Cheran, Tarantino, Gori, Retico, De Nunzio, Fantacci, Masala, Teresa Tarantino

Risultato della ricerca: Otherpeer review

7 Citazioni (Scopus)

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 originaleEnglish
Pagine351-372
Numero di pagine22
Stato di pubblicazionePublished - 2007

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.2200.2204???
  • ???subjectarea.asjc.2700.2741???
  • ???subjectarea.asjc.1700.1707???
  • ???subjectarea.asjc.2700.2718???
  • ???subjectarea.asjc.1700.1706???
  • ???subjectarea.asjc.1700.1704???

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