Multi-level Ground Glass Nodule Detection and Segmentation in CT Lung Images

Early detection of Ground Glass Nodule (GGN) in lung Computed Tomography (CT) images is important for lung cancer prognosis. Due to its indistinct boundaries, manual detection and segmentation of GGN is labor-intensive and problematic. In this paper, we propose a novel multi-level learning-based framework for automatic detection and segmentation of GGN in lung CT images. Our main contributions are: firstly, a multi-level statistical learning-based approach that seamlessly integrates segmentation and detection to improve the overall accuracy for GGN detection (in a subvolume). The classification is done at two levels, both voxel-level and object-level. The algorithm starts with a three-phase voxel-level classification step, using volumetric features computed per voxel to generate a GGN class-conditional probability map. GGN candidates are then extracted from this probability map by integrating prior knowledge of shape and location, and the GGN object-level classifier is used to dete...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Yimo Tao, Le Lu, Maneesh Dewan, Albert Y. Chen, Jason J. Corso, Jianhua Xuan, Marcos Salganicoff, Arun Krishnan
• Keywords: Detection, Lung Cancer Prognosis, Lung Computed Tomography, Medical Imaging, MICCAI 2009