Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 39, Issue 12, Pages 1137-1144Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2009.10.005
Keywords
Computer-aided detection (CAD); Low-dose computed tomography (LDCT); Thin-slice CT; Lung nodule; Directional-gradient concentration; Morphological operators; Neural networks
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Funding
- Italian Istituto Nazionale di Fisica Nucleare (INFN) [CSN 5]
- ministero dell'Universita e della Ricerca (MIUR)
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A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (Cr) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by 12 morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a clataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level I (nodules annotated by only one radiologist) with six FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists). (C) 2009 Elsevier Ltd. All rights reserved.
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