Automatic Detection and Segmentation of Ground Glass Opacity Nodules

Ground Glass Opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (nonsolid) or mixed (partially solid) GGO at the thinsection 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 inter- or 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-NN whose distance measure is the Euclidean distance between the nonparametric density estimates of two examples. The detected GGO region is then automatically segmented by analyzing the texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed me...

• Type: Conference
• Year: 2006
• Where: MICCAI
• Authors: Jinghao Zhou, Sukmoon Chang, Dimitris N. Metaxas, Binsheng Zhao, Lawrence H. Schwartz, Michelle S. Ginsberg
• Keywords: Automatic Ggo Segmentation, GGO Detection, GGO Nodules, Medical Imaging, MICCAI 2006