1 Signal and image processing laboratory, School of Electronic Information Engineering, Beijing Jiao tong University , Beijing , China.
2 Department of Oral and Maxillofacial Radiology, Peking University, School and Hospital of Stomatology , Beijing , China.
Dentomaxillofac Radiol. 2019 Feb;48(2):20180236. doi: 10.1259/dmfr.20180236. Epub 2018 Oct 11.
: A method was proposed to segment the tooth pulp cavity region in cone beam CT) images, which aimed to make the extraction process more efficient and generate more reliable results for further research.
: Cone beam CT images of 50 teeth from 10 patients were randomly collected with the help of Peking University Hospital of Stomatology. All slice images have a ground truth tooth pulp cavity region delineated by two doctors manually. After necessary gamma transform in pre-processing stage, three kinds of information in an image such as greyscale, neighbour average greyscale and gradient were fused to search an optimal segmentation threshold by using plane intercept histogram of reciprocal cross entropy algorithm. With the optimal threshold, binarization was conducted and the tooth pulp cavity regions in slice images can be extracted. Qualitative and quantitative analyses compared to ground truth are involved with the evaluation criterion of average non-coincidence rate ( ). Independent repeated experiments were carried out to test the stability of this segmentation method.
: Accurate and complete segmentation results are obtained. The proposed method reaches the lowest values in most cases and owns more competitive robustness under various interferences compared with the other popular segmentation methods like reciprocal cross entropy method, active contour-based method, region growing method and level set method. Quantitative analysis verified the effectiveness of this method.
: The proposed method can extract tooth pulp cavity regions from teeth efficiently. The segmentation results of this method are more accurate compared to other popular methods under different circumstances and can be used for subsequent applications.
提出一种分割锥形束 CT 图像牙髓腔区域的方法，旨在提高提取过程的效率，并为进一步的研究提供更可靠的结果。
本研究随机收集了北京大学口腔医院 10 名患者的 50 颗牙齿的锥形束 CT 图像。所有切片图像都有两位医生手动勾勒的牙髓腔区域的真实边界。在预处理阶段进行必要的伽马变换后，通过倒数互信息算法的平面截距直方图融合图像中的灰度、邻域平均灰度和梯度等三种信息，搜索最佳分割阈值。利用最佳阈值进行二值化，可以提取切片图像中的牙髓腔区域。采用平均不一致率（ ）进行定性和定量分析来评估。采用独立重复实验来测试该分割方法的稳定性。
得到了准确完整的分割结果。与其他流行的分割方法（如倒数互信息法、基于活动轮廓的方法、区域生长法和水平集法）相比，该方法在大多数情况下达到了最低的 值，在各种干扰下具有更强的鲁棒性。定量分析验证了该方法的有效性。
该方法可以有效地从牙齿中提取牙髓腔区域。与其他流行方法相比，该方法在不同情况下的分割结果更准确，可用于后续应用。
