|Place of Conferral||北京|
|Keyword||齿脸型 深度图像 几何信息 特征提取 相关性|
|Other Abstract|| 牙齿修复是口腔医学研究中的重要课题。在现代口腔修复医学中，选择合适的牙齿齿型以满足患者的生理、心理和社会需要成为齿型选择的重要标准。选择的齿型不但要具有自然美，还要具有社会美，即齿型应与患者的审美意识、心理、性别、年龄和职业属性等相一致。在全牙的齿型选择中，上颌中切牙的选择较为重要。在临床实践中，医生普遍通过患者的脸型来判断上颌中切牙的形状、大小、位置等信息。|
; The restoration of missing teeth is an important topic in oral rehabilitation medicine. Particularly, in the modern oral rehabilitation medicine, the restoration of missing teeth has to meet the patient's physiological, psychological, and social needs, which has been the standard of tooth selection. The restored teeth should not only have natural beauty, but also society beauty. i.e.e, the restored teeth should be consistent with the patient's aesthetic consciousness, psychology, gender, age and occupation, etc. In the oral rehabilitation medicine, the restoration of maxillary central incisors is the most important topic. In clinical practice, doctors usually select maxillary central incisors, including shape, size, location and other information, according to the patient's face.
This study focuses on the relationship between shapes of maxillary central incisors data and face data. Our study adopts 3D images of teeth and faces, which provide richer information than traditional 2D images. From the 3D data, we extract depth features and investigate relationships between shapes of faces and teeth, which could guide tooth restoration in clinical practice. This thesis consists of following parts:
（1）We utilize 3D range images to extract features of shapes of faces and teeth. We obtain triangular mesh surface data from the Peking University School of Stomatology. After preprocessing and orthogonal projection, 3D range images are obtained by resampling and regularizing 3D depth values . We adopt K-means clustering method to cluster range images of faces and teeth respectively, in order to validate the correspondence between the data driven clustering results. We found that that face models were different in their contour and depth of cheek, while maxillary central incisors were different in their contour and dental ridges. Statistical analysis results further indicated that face data of long type and maxillary central incisors of long type were correlated with each other. We also found stronger correlation between shapes of teeth and faces in males than in females, and no significant correlation between maxillary central incisors of square type and face of square type was found for females.
（2）We utilize geometric information and contour information to extract shape features of faces and teeth. We utilize curvature information to extract symmetry plane and contour line on 3D face model, and then obtain facial feature points. For 3D tooth model, we utilize curvature information and cubic curve fitting to extract teeth ridge and contour line. In order to evaluate the accuracy and reliability of our method, we extract features of different types of face models and tooth models, and then analyze the difference between different types of teeth and faces in terms of curvature and contour.
（3）We adopt canonical correlation analysis to analyze the relationship between tooth and face shape models. Based on 3D models of faces and teeth that have been registered, canonical correlation analysis is utilized to investigate relationships between faces and teeth with respect to global and local features, respectively. Experimental results have demonstrated that length and width of the face model are correlated with features of maxillary central incisors in a high-dimensional space, indicating that the length and the width of faces might be helpful to predict the shape of teeth.
|陈锐. 齿脸形状匹配关系研究[D]. 北京. 中国科学院大学,2016.|
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