基于水平集的牙齿CT图像分割技术

Development of teeth segmentation from computed tomography images using level set method

牙齿的计算机断层扫描(CT)图像中存在边界模糊、相邻牙齿粘连等情况,且拓扑结构较为复杂,要实现准确的牙齿分割非常困难。对传统的牙齿CT图像分割方法,特别是近年来用于牙齿分割的水平集方法进行介绍,对其水平集函数中各能量项进行研究,并通过对比实验体现水平集方法的优越性。基于水平集的牙齿CT图像分割方法中水平集函数的能量项主要包括:竞争能量项、梯度能量项、形状约束能量项、全局先验灰度能量项、局部灰度能量项。实验结果表明基于混合模型的水平集方法分割效果最佳,切牙与磨牙分割准确率分别为88.92%和92.34%,相比自适应阈值和传统水平集方法,分割准确率总体提升10%以上。在综合利用图像信息和先验知识的基础上,通过对水平集函数中能量项进行优化和创新,有望进一步提高分割的准确率。

In oral surgery,segmentation of teeth has important application value. However,due to the ambiguity of tooth boundary,the adhesion of adjacent teeth,and the flexible change of topological structure in dental Computer Tomography（ CT） images,it is very difficult to achieve the accurate segmentation. To provide a useful reference for researches,this paper explored the search progress of dental CT image segmentation base on level set methods,summarized the traditional methods of dental CT images segmentation,introduced the level set theory briefly,introduced the details of level set methods for teeth segmentation in recent years,studied the energy terms in level set function,and implemented some contrast experiments. In the dental CT images segmentation based on level set method,the energy terms mainly included competitive energy,edge energy,shape prior energy,global intensity prior energy and local intensity energy. The experimental results show that the performance of hybrid model of the level set method is the best. The segmentation accuracies of incisor and molar teeth were88. 92% and 92. 34% respectively. Compared to the method of adaptive threshold and level set without re-initialization,the accuracy of hybrid model improved more than 10% overall. With the utilization of image information and prior knowledge,it is expected to improve the accuracy of segmentation by optimizing and innovating the energy term in the level set function.