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28 February 2013 Robust airway extraction based on machine learning and minimum spanning tree
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Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700L (2013)
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Recent advances in MDCT have improved the quality of 3D images. Virtual Bronchoscopy has been used before and during the bronchoscopic examination for the biopsy. However, Virtual Bronchoscopy has become widely used only for the examination of proximal airway diseases. The reason is that conventional airway extraction methods often fail to extract peripheral airways with low image contrast. In this paper, we propose a machine learning based method which can improve the extraction robustness remarkably. The method consists of 4 steps. In the first step, we use Hessian analysis to detect as many airway candidates as possible. In the second, false positives are reduced effectively by introducing a machine learning method. In the third, an airway tree is constructed from the airway candidates by utilizing a minimum spanning tree algorithm. In the fourth, we extract airway regions by using Graph cuts. Experimental results evaluated by a standardized evaluation framework show that our method can extract peripheral airways very well.
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Tsutomu Inoue, Yoshiro Kitamura, Yuanzhong Li, and Wataru Ito "Robust airway extraction based on machine learning and minimum spanning tree", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700L (28 February 2013);

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