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.