TY - GEN
T1 - An automatic method for ground glass opacity nodule detection and segmentation from CT studies
AU - Zhou, Jinghao
AU - Chang, Sukmoon
AU - Metaxas, Dimitris N.
AU - Zhao, Binsheng
AU - Ginsberg, Michelle S.
AU - Schwartz, Lawrence H.
PY - 2006
Y1 - 2006
N2 - Ground Glass Opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (non-solid) or mixed (partially solid) GGO at the thin-section CT are more likely to be malignant than those with solid opacity, early detection and treatment of GGO can improve a prognosis of lung cancer. However, due to indistinct boundaries and interor intra-observer variation, consistent manual detection and segmentation of GGO have proved to be problematic. In this paper, we propose a novel method for automatic detection and segmentation of GGO from chest CT images. For GGO detection, we develop a classifier by boosting K-Nearest Neighbor (K-NN), whose distance measure is the Euclidean distance between the nonparametric density estimates of two regions. The detected GGO region is then automatically segmented by analyzing the 3D texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed method detected all of the 10 nodules with only one false positive nodule. We also present the statistical validation of the proposed classifier for automatic GGO detection as well as very promising results for automatic GGO segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of GGO.
AB - Ground Glass Opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (non-solid) or mixed (partially solid) GGO at the thin-section CT are more likely to be malignant than those with solid opacity, early detection and treatment of GGO can improve a prognosis of lung cancer. However, due to indistinct boundaries and interor intra-observer variation, consistent manual detection and segmentation of GGO have proved to be problematic. In this paper, we propose a novel method for automatic detection and segmentation of GGO from chest CT images. For GGO detection, we develop a classifier by boosting K-Nearest Neighbor (K-NN), whose distance measure is the Euclidean distance between the nonparametric density estimates of two regions. The detected GGO region is then automatically segmented by analyzing the 3D texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed method detected all of the 10 nodules with only one false positive nodule. We also present the statistical validation of the proposed classifier for automatic GGO detection as well as very promising results for automatic GGO segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of GGO.
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U2 - 10.1109/IEMBS.2006.260285
DO - 10.1109/IEMBS.2006.260285
M3 - Conference contribution
C2 - 17946543
SN - 1424400325
SN - 9781424400324
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 3062
EP - 3065
BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Y2 - 30 August 2006 through 3 September 2006
ER -