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3D GGO candidate extraction in lung CT images using multilevel thresholding on supervoxels
Author(s): Shan Huang; Xiabi Liu; Guanghui Han; Xinming Zhao; Yanfeng Zhao; Chunwu Zhou
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Paper Abstract

The earlier detection of ground glass opacity (GGO) is of great importance since GGOs are more likely to be malignant than solid nodules. However, the detection of GGO is a difficult task in lung cancer screening. This paper proposes a novel GGO candidate extraction method, which performs multilevel thresholding on supervoxels in 3D lung CT images. Firstly, we segment the lung parenchyma based on Otsu algorithm. Secondly, the voxels which are adjacent in 3D discrete space and sharing similar grayscale are clustered into supervoxels. This procedure is used to enhance GGOs and reduce computational complexity. Thirdly, Hessian matrix is used to emphasize focal GGO candidates. Lastly, an improved adaptive multilevel thresholding method is applied on segmented clusters to extract GGO candidates. The proposed method was evaluated on a set of 19 lung CT scans containing 166 GGO lesions from the Lung CT Imaging Signs (LISS) database. The experimental results show that our proposed GGO candidate extraction method is effective, with a sensitivity of 100% and 26.3 of false positives per scan (665 GGO candidates, 499 non-GGO regions and 166 GGO regions). It can handle both focal GGOs and diffuse GGOs.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057533 (27 February 2018); doi: 10.1117/12.2293217
Show Author Affiliations
Shan Huang, Beijing Institute of Technology (China)
Xiabi Liu, Beijing Institute of Technology (China)
Guanghui Han, Beijing Institute of Technology (China)
Xinming Zhao, Chinese Academy of Medical Sciences (China)
Peking Union Medical College (China)
Yanfeng Zhao, Chinese Academy of Medical Sciences (China)
Peking Union Medical College (China)
Chunwu Zhou, Chinese Academy of Medical Sciences (China)
Peking Union Medical College (China)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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