Shenzhen Graduate School, Harbin Institute of Technology (HIT), HIT Campus of University Town of Shenzhen, Shenzhen, China.
School of Computer Science and Information Engineering, Guangxi Normal University, 15 Yucai Road, Qixing District, Guilin, China.
Comput Biol Med. 2018 Dec 1;103:208-219. doi: 10.1016/j.compbiomed.2018.10.026. Epub 2018 Oct 25.
The accuracy of extraction of biological characteristic curves in tooth preparations directly determines whether the tooth restorations and preparations are closely matched to allow appropriate adhesion. Ultimately, these will affect the success of the dental restoration surgery. In the process to obtain the tooth preparation, the dentist is required to grind the tooth manually and fuzzy regions may thus exist. Multiple feature curves with locally increased curvatures exist in these fuzzy regions, but only the outermost is preferred. The characteristic curve consists of points, some with and some without extreme curvature values. This study aims to extract an accurate biological characteristic curve.
This challenging problem is mapped to the search of the minimum cost path for a graph, and is solved using the well-known A* algorithm. To identify the mapped graph, the outward direction coefficient is first introduced followed by the extremality coefficient node. Both of these coefficients ensure that the biological characteristic curve can be accurately extracted.
The conducted experiment demonstrated that the proposed algorithm can rapidly, accurately, and automatically obtain the outermost feature curve which passes through the fuzzy region of the tooth preparation. Additionally, the part of the biological characteristic curve related to the non-fuzzy region can also be accurately extracted.
The proposed algorithm significantly improves the accuracy of the extraction curve and the quality of the restoration design.
牙体预备中生物特征曲线的提取精度直接决定了牙体修复体和预备体是否紧密匹配,从而实现适当的粘结。最终,这些将影响牙科修复手术的成功。在获得牙体预备的过程中,牙医需要手动磨牙,因此可能存在模糊区域。这些模糊区域中存在多个局部曲率增加的特征曲线,但只需要最外层的曲线。特征曲线由点组成,有些点有极值曲率值,有些没有。本研究旨在提取准确的生物特征曲线。
将这个具有挑战性的问题映射到图的最小代价路径搜索问题,并使用著名的 A*算法来解决。为了识别映射的图,首先引入了外向系数,然后是极值系数节点。这两个系数都确保了可以准确地提取生物特征曲线。
进行的实验表明,所提出的算法可以快速、准确、自动地获取通过牙体预备模糊区域的最外层特征曲线。此外,还可以准确地提取与非模糊区域相关的生物特征曲线部分。
所提出的算法显著提高了提取曲线的准确性和修复设计的质量。