基于双层回归森林模型的头影测量图像结构特征点自动定位

doi:10.3969/j.issn.0255-8297.2019.04.005

Abstract

Abstract: To automatically detect anatomical landmarks in cephalometric X-Ray images, a context-aware landmark detection method using two-layer regression forest models is proposed. First, it extracts appearance features from images to train the first-layer regression forest model, which can be used to generate a displacement map for each landmark per training image. Second, from the displacement maps, the context features are computed and combined with appearance features to train the second-layer regression forest. Then, by exerting the trained two-layer regression forest model on the new cephalometric X-Ray images to be processed, the displacement vectors of all pixels to each target landmark will be produced. Finally, the proposed method uses regression voting to acquire the landmark position in the testing image. Experimental results show that the proposed method has good performance in the detection of cephalometric landmarks in dental X-Ray images.

Key words: lateral cephalogram, anatomical landmark detection, context feature, regression forest

CLC Number:

TP391

QIN Zhen, DAI Xiubin, XIE Lizhe. Anatomical Landmark Localization in Lateral Cephalograms by Using Two-Layer Regression Forests[J]. Journal of Applied Sciences, 2019, 37(4): 481-489.

References

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Anatomical Landmark Localization in Lateral Cephalograms by Using Two-Layer Regression Forests

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