Automated 2-D cephalometric analysis on X-ray images by a model-based approach
Automated 2-D cephalometric analysis on X-ray images by a model-based approach
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DOI：
10.1109/tbme.2006.876638
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发表时间：
2006-08-01
影响因子：
4.6
通讯作者：
Xu, Tianmin
中科院分区：
文献类型：
--
作者：
Yue, Weining;Yin, Dali;Xu, Tianmin
颅面标志点定位和解剖结构追踪是获得头影测量分析的两个重要方法。为了并行计算，提出了一种基于模型的方法来定位262个颅面特征点，其中包括90个标志点和172个辅助点。在模型训练中，选取12个界标作为参考点，根据解剖学知识将每个训练形状划分为10个区域，采用主成分分析法表征区域形状变化和每个特征点的统计灰度轮廓。在输入图像上定位特征点是两阶段过程。首先，我们。通过图像处理和模式匹配技术识别所述参考界标，从而对所述输入图像执行所述形状分割。然后，对于每个区域，其特征点定位的修改后的主动形状模型。根据先验知识，用细分曲线连接定位点，即可绘出颅面各解剖结构。允许用户以多种不同的方式交互地修改结果。实验结果表明了该方法的优越性和可靠性。
Craniofacial landmark localization and anatomical structure tracing on cephalograms are two important ways to obtain the cephalometric analysis. In order to-computerize them in parallel, a model-based approach is proposed to locate 262 craniofacial feature points, including 90 landmarks and 172 auxiliary points. In model training, 12 landmarks are selected as reference points and used to divide every training shape to 10 regions according to the anatomical knowledge; principle components analysis is employed to characterize the region shape variations and the statistical grey profile of every feature point. Locating feature points on an input image is a two-stage procedure. First, we. identify the reference landmarks by image processing and pattern matching techniques, so that the shape partition is performed on the input image. Then, for each region, its feature points are located by a modified active shape model. All craniofacial anatomical structures can be traced out by connecting the located points with subdivision curves according to the prior knowledge. Users are permitted to modify the results interactively in many different ways. Experimental results show the advantage and reliability of the proposed method.