Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images

The automatic detection of lung nodules attached to other pulmonary structures is a useful yet challenging task in lung CAD systems. In this paper, we propose a stratified statistical learning approach to recognize whether a candidate nodule detected in CT images connects to any of three other major lung anatomies, namely vessel, fissure and lung wall, or is solitary with background parenchyma. First, we develop a fully automated voxel-by-voxel labeling/segmentation method of nodule, vessel, fissure, lung wall and parenchyma given a 3D lung image, via a unified feature set and classifier under conditional random field. Second, the generated Class Probability Response Maps (PRM) by voxel-level classifiers, are used to form the so-called pairwise Probability Co-occurrence Maps (PCM) which encode the spatial contextual correlations of the candidate nodule, in relation to other anatomical landmarks. Based on PCMs, higher level classifiers are trained to recognize whether the nodule touche...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Kim Boyer, Arun Krishnan, Marcos Salganicoff
• Keywords: Candidate Nodule, Computer Vision, CVPR 2010, Lung, Lung Wall