Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest

In order to solve the problems of sensitive initial contours and inaccurate segmentation caused by active contour segmentation of CT images, this paper proposes an automatic 3D vertebral CT active contour segmentation method combined weighted random forest called “WRF-AC”. This method proposes a weighted random forest algorithm and an active contour energy function that includes edge energy. First, the weighted random forest is trained by extracting 3D Haar-like feature values of the vertebra CT, and the 'vertebra center' obtained is used as the initial contour of the segmentation. Then, the segmentation of the vertebra CT image is completed by solving the active contour energy function minimum containing the edge energy. The experimental results show that this method can segment the spine CT images more accurately and quickly on the same datasets to extract the vertebrae.

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Keywords:

3D segmentation; CT images; weighted random forest; active contour

Funds:

National Natural Science Foundation of China (61172167) and Natural Science Foundation of Heilongjiang Province (QC2017076)

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Get Citation: Liu Xia, Gan Quan, Li Bing, et al. Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest[J]. Opto-Electronic Engineering, 2020, 47(12): 200002.

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