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CVPR
2010
IEEE

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

13 years 10 months ago
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...
Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Ki
Added 08 Aug 2010
Updated 08 Aug 2010
Type Conference
Year 2010
Where CVPR
Authors Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Kim Boyer, Arun Krishnan, Marcos Salganicoff
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