Automatic Analysis of Cephalogram for Orthodontics

（1995）作者在1992年提出了一种新的模糊神经元，对其进行了泛化和修正，我们称之为广义模糊学习机。本机能很好地掌握各输入输出的非线性相关性。与传统的神经网络相比，它具有很高的非线性映射能力，并能保证全局最小值。此外，该方法的学习速度和准确性都得到了极大的提高，并成功地应用于正畸治疗中x线体表标记位置的自动检测。（1996）提出了一种利用神经网络和模糊模板匹配的方法提取x线造影脑电图中的标志。针对模糊模板匹配问题，提出了两种加权相似度度量方法。首先通过神经网络找到地标所在的粗糙区域。然后对该区域进行模糊模板匹配，以找到其地标的确切位置。在实际使用的允许误差范围内，成功地在实际x线造影脑电图中发现了典型的地标。（1997）提出了用径向基函数网络（RBFN）预测颅面复合体生长的方法。颅面复合体的生长预测在口腔正畸学中非常重要，因为如果预测不准确，就需要再次手术，这将给患者带来身体和/或精神上的痛苦。首先通过模糊聚类将学习数据划分为3个骨架组，然后对每一组构建RBFN。通过对每个RBFN的输出进行加权和来进行预测。预测结果令人鼓舞。

(1995)A neo-fuzzy-neuron, presented by the authors in 1992, was generalized and modified, which we call a generalized fuzzy learning machine. This machine can well grasp the nonlinear correlation of each input and output. It has a very high nonlinear mapping ability compared with the conventional neural network, and it guaranteesa global minimum. Furthermore, the learning speed and its accuracy are improved drastically, It was successfully applied to the automatic detection of landmark positions in the roentgenographic cephalogram for an orthodontic treatment.(1996)An extraction of landmarks in a roentgenographic cephalogram by using a neural network and a fuzzy template matching was proposed. Two kinds of weighted similarity measures are newly proposed for a fuzzy template matching. The rough region where a landmark is supposed to be located is first found out by a neural network. The fuzzy template matching is then performed over this region to find the exact location of its landmark. Typical landmarks were successfully found in the actual roentgenographic cephalogram within a permissible error for a practical use.(1997)Growth prediction of craniofacial complex by using an RBFN(Radial Basis Function Network) was proposed. The growth prediction of craniofacial complex is very important in the field of orthodontics, because if it is not well predicted re-operation would be necessary, which causes physical and/or mental pain to a patient. A set of learning data was first divided into three skeletal groups by Fuzzy clustering, and then RBFN was constructed for each cluster. The prediction was performed by taking the weighted sum of the outputs of each RBFN.The prediction results were promising.