TY - GEN
T1 - Vector quantization-based automatic detection of pulmonary nodules in thoracic CT images
AU - Han, Hao
AU - Li, Lihong
AU - Han, Fangfang
AU - Zhang, Hao
AU - Moore, William
AU - Liang, Zhengrong
PY - 2013
Y1 - 2013
N2 - Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel CADe system for lung nodule detection based on a vector quantization (VQ) approach. Compared to existing CADe systems, the extraction of lungs from the chest CT image is fully automatic, and the detection and segmentation of initial nodule candidates (INCs) within the lung volume is fast and accurate due to the self-adaptive nature of VQ algorithm. False positives in the detected INCs are reduced by rule-based pruning in combination with a feature-based support vector machine classifier. We validate the proposed approach on 60 CT scans from a publicly available database. Preliminary results show that our CADe system is effective to detect nodules with a sensitivity of 90.53 % at a specificity level of 86.00%.
AB - Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel CADe system for lung nodule detection based on a vector quantization (VQ) approach. Compared to existing CADe systems, the extraction of lungs from the chest CT image is fully automatic, and the detection and segmentation of initial nodule candidates (INCs) within the lung volume is fast and accurate due to the self-adaptive nature of VQ algorithm. False positives in the detected INCs are reduced by rule-based pruning in combination with a feature-based support vector machine classifier. We validate the proposed approach on 60 CT scans from a publicly available database. Preliminary results show that our CADe system is effective to detect nodules with a sensitivity of 90.53 % at a specificity level of 86.00%.
UR - https://www.scopus.com/pages/publications/84904164854
U2 - 10.1109/NSSMIC.2013.6829365
DO - 10.1109/NSSMIC.2013.6829365
M3 - Conference contribution
AN - SCOPUS:84904164854
SN - 9781479905348
T3 - IEEE Nuclear Science Symposium Conference Record
BT - 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 60th IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2013
Y2 - 27 October 2013 through 2 November 2013
ER -