Automated Detection of Small-Size Pulmonary Nodules Based on Helical CT Images

Abstract. A computer-aided diagnosis (CAD) system to detect smallsize (from 2 mm to around 10 mm) pulmonary nodules in helical CT scans is developed. This system uses different schemes to locate juxtapleural nodules and non-pleural nodules. For juxtapleural nodules, morphological closing, thresholding and labeling are performed to obtain volumetric nodule candidates; gray level and geometric features are extracted and analyzed using a linear discriminant analysis (LDA) classifier. To locate non-pleural nodules, a discrete-time cellular neural network (DTCNN) uses local shape features which successfully capture the differences between nodules and non-nodules, especially vessels. The DTCNN was trained using genetic algorithm (GA). Testing on 17 cases with 3979 slice images showed the effectiveness of the proposed system, yielding sensitivity of 85.6% with 9.5 FPs/case (0.04 FPs/image). Moreover, the CAD system detected many nodules missed by human visual reading. This showed that the...

• Type: Conference
• Year: 2005
• Where: IPMI
• Authors: Xiangwei Zhang, Geoffrey McLennan, Eric A. Hoffman, Milan Sonka
• Keywords: IPMI 2005, Juxtapleural Nodules, Medical Imaging, Non-pleural Nodules, Pulmonary Nodules