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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu, D. Le Lu Jinbo Bi Shinagawa, Y. Boyer, K. Krishnan, A. Salganicoff, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: ECSE Department, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (Boyer, K.) || CAD and Knowledge Solutions, Siemens Medical Solutions, Malvern, PA 19355 USA (Wu, D.; Le Lu; Jinbo Bi; Shinagawa, Y.; Krishnan, A.; Salganicoff, M.) |
| Abstract | 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 touches other pulmonary structures, as a multi-label problem. We also present a new iterative fissure structure enhancement filter with superior performance. For experimental validation, we create an annotated database of 784 subvolumes with nodules of various sizes, shapes, densities and contextual anatomies, and from 239 patients. High accuracy of multi-class voxel labeling is achieved 89.3% ∼ 91.2%. The Area under ROC Curve (AUC) of vessel, fissure and lung wall connectivity classification reaches 0.8676, 0.8692 and 0.9275, respectively. |
| Starting Page | 2791 |
| Ending Page | 2798 |
| File Size | 1920212 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540008 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Lungs Computed tomography Anatomy Labeling Phase change materials Statistical learning Image recognition Image segmentation Filters Databases |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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