Automatic Analysis of Cephalogram for Orthodontics

正畸头影自动分析

• 批准号: 07680948
• 负责人: UCHINO Eiji
• 金额: $ 1.15万
• 依托单位: KYUSHU INSTITUTE OF TECHNOLOGY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1995
• 资助国家: 日本
• 起止时间: 1995 至 1997
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680948/
• 关键词: Orthodontic Treatment; Cephalogram; Cephalo Analysis; Fuzzy Inference; Neural Network; Neo-Fuzzy Neuron; Fuzzy Clustering; Fuzzy Template Matching; ファジィクラスリング; ファジイ推論

(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.

(1995)对作者在1992年提出的一种新模糊神经元进行了推广和改进，我们称之为广义模糊学习机。该机器能够很好地把握各输入输出之间的非线性相关性。与传统的神经网络相比，它具有很高的非线性映射能力，并且保证了全局最小。此外，它的学习速度和精度都有了很大的提高，并成功地应用于X线片上标志点位置的自动检测，用于正畸治疗。(1996)提出了一种基于神经网络和模糊模板匹配的X线片标志点提取方法。针对模糊模板匹配问题，提出了两种加权相似性度量方法。首先通过神经网络找出地标应该位于的粗略区域。然后对该区域执行模糊模板匹配，以找到其地标的准确位置。在实际X线片中成功地找到了典型的标志点，误差在允许的范围内。(1997)提出了用径向基函数神经网络预测颅面复合体生长的方法。颅面复合体的发育预测在正畸领域是非常重要的，因为如果不能很好地预测它，就需要再次手术，这会给患者带来生理和/或精神上的痛苦。首先通过模糊聚类将一组学习数据划分为三个骨架类，然后为每个类构建RBFN。预测采用各径向基神经网络输出的加权和，预测结果令人满意。

项目成果

Takeshi Yamakawa and Eiji Uchino: "Neo-Fuzzy-Neuron and Its Learning Algorithms with Applications to the Modeling of Nonlinear Dynamical Systems" in "Applications of Fuzzy Logic : Towards High MachineIntelligence Quotient Systems" eds.M.Jamshidi, A.Titli,

Takeshi Yamakawa 和 Eiji Uchino：“模糊逻辑的应用：走向高机器智商系统”中的“新模糊神经元及其学习算法及其在非线性动力系统建模中的应用”，eds.M.Jamshidi，A.Titli，

山川烈: "セファロ画像における重み付き類似性測度を用いた計測点の抽出" Biomedical Fuzzy and Human Science. Vol.2,No.1. 93-101 (1996)

Retsu Yamakawa：“在头影测量图像中使用加权相似性测量来提取测量点”《生物医学模糊与人类科学》第 2 卷，第 93-101 期（1996 年）。

Eiji Uchino: "High Speed Fuzzy Learning Machine with Guarantee of Global Minimum and Its Application to Chaotic System Identification and Medical Image Processing" International Journal on Artificial Intelligence Tools. Vol.5,Nos.1&2. 23-39 (1996)

Eiji Uchino：“保证全局最小值的高速模糊学习机及其在混沌系统识别和医学图像处理中的应用”国际人工智能工具杂志。

Eiji Uchino: "Nonlinear Modeling and Filtering by RBF Network with Application to Noisy Signal" Journal of Information Sciences. Vol.101. 177-185 (1997)

Eiji Uchino：“RBF 网络的非线性建模和滤波及其在噪声信号中的应用”信息科学杂志。

森下雅子: "トレース線図形を用いない直接的な計測点抽出法" 第55回日本矯正歯科学会大会抄録集. 153-153 (1996)

Masako Morishita：“不使用轨迹线图的直接测量点提取方法”第 55 届日本正畸学会会议记录 153-153（1996 年）。