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3 March 2009 A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601S (2009)
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
The computer-aided detection (CAD) system we applied on the ANODE09 dataset is devoted to identify pulmonary nodules in low-dose and thin-slice computed tomography (CT) images: we developed two different systems for internal (CADI) and juxtapleural nodules (CADJP) in the framework of the italian MAGIC-5 collaboration. The basic modules of CADI subsystem are: a 3D dot-enhancement algorithm for nodule candidate identification and an original approach, we referred as Voxel-Based Neural Approach (VBNA), to reduce the amount of false-positive findings based on a neural classifier working at the voxel level. To detect juxtapleural nodules we developed the CADJP subsystem based on a procedure enhancing regions where many pleura surface normals intersect, followed by a VBNA classification. We present both the FROC curves we obtained on the 5 annotated ANODE09 example dataset, and on all the ANODE09 50 test cases.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alessandra Retico, Francesco Bagagli, Niccolo Camarlinghi, Carmela Carpentieri, Maria Evelina Fantacci, and Ilaria Gori "A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601S (3 March 2009);

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