[A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting].

Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar's structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels' variance and mean values, which improves the algorithm's robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms' results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.口腔牙齿图像分割对于牙齿的正畸手术、义齿种植等有重要的意义。由于牙根被牙槽骨包围，磨牙拓扑结构复杂，以及在牙齿内部有牙髓腔的存在，导致分割时易出现过分割、内部轮廓等问题。为进一步提高分割精度，针对上述问题本文提出了一种基于局部高斯分布拟合与边缘检测相结合的分割算法。该算法融合了像素局部邻域的方差和均值，并将图像梯度信息引入边缘检测，提高了分割算法的稳定性。在实验中，基于锥形束计算机断层图像，完成了对牙根图像的精确分割，并与当前比较经典的算法进行了对比，结果表明，本文算法能够更好地区分牙根以及牙根周围的牙槽骨，能够更精确地分割出牙根及分裂的磨牙，没有出现牙髓腔内部轮廓的情况。.