基于水平集的牙齿牙槽骨图像分割
收稿日期: 2020-04-19
网络出版日期: 2020-05-27
基金资助
上海市自然科学基金资助项目(18ZR1426900);上海市大学生创新创业训练计划(SH2019166)
Image Segmentation of Tooth and Alveolar Bone with the Level Set Model
Received date: 2020-04-19
Online published: 2020-05-27
口腔锥形束计算机断层扫描（Cone Beam Computed Tomography，CBCT）图像中牙齿及牙槽骨的分割对骨性结构的三维重建提供了基础，是实现牙齿牙槽骨三维可视化的必要步骤.本文根据牙齿及牙槽骨特点，将一种改进的势阱函数与水平集模型结合，克服以往势阱函数在部分区域出现“停止演化”或“过快演化”的缺陷，并将其应用在对牙齿牙槽骨的分割当中.采用多次小方差高斯滤波叠加的方式对图像进行序贯滤波预处理，解决单一方差高斯滤波难以有效滤除CBCT图像中噪声的问题，为准确分割提供了条件；基于序列图像相邻两张图片中同一牙齿的轮廓变化不大这一特点，以当前层的分割结果作为下一层曲线演化的初始轮廓，使得用更少的迭代次数得到相同结果，从而提高分割速度.另外，本文还将该算法应用于口腔磁共振图像中，并成功对单颗牙齿进行了分割.
石沁祎 , 闫方 , 杨阳 , 陈玥甫 , 林晓浪 , 王远军 . 基于水平集的牙齿牙槽骨图像分割[J]. 波谱学杂志, 2021 , 38(2) : 182 -193 . DOI: 10.11938/cjmr20202827
Segmentation of tooth and alveolar bone from the cone beam computed tomography (CBCT) images provides the basic data for the three-dimensional reconstruction and visualization of bone structure. In this paper, according to the characteristics of tooth and alveolar bone, an improved potential well function was combined with the level set model for segmentation of tooth and alveolar bone, overcoming the defects of 'stop evolution' or 'too fast evolution' that might occur with the use of conventional potential well functions. Since it is difficult to effectively filter out the noises in CBCT image with the single variance Gaussian filter, a multiple small variance Gaussian filter stack was used to preprocess the image. As the contours of the same tooth in adjacent images of the image sequence showed only little changes, the segmentation result of the current layer was taken as the initial contour of the curve evolution for the next layer to reduce the times of iteration and increase the speed of segmentation. In addition, the algorithm is also used to segment a single tooth in magnetic resonance image of oral cavity successfully.
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