为了实现X射线头影测量图像中结构特征点的自动定位，提出一种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方法.首先从图像中提取外观特征训练第1层回归森林模型，通过该模型生成针对当前特征点位置的偏移距离图；然后从偏移距离图中提取上下文特征，并结合外观特征训练第2层回归森林模型；接着将双层回归森林模型用于待检测的X射线头影测量图像，预测出图像中每个像素关于目标特征点的偏移距离；最后根据回归投票方法求得结构特征点位置.实验结果表明，基于双层回归森林模型的自动定位方法能较准确地获得头影测量图像中结构特征点的位置.
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.
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