Tam Weng-Kong, Lee Hsi-Jian
Institute of Medical Sciences, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien City, Hualien County 97004, Taiwan, ROC.
Institute of Medical Sciences, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien City, Hualien County 97004, Taiwan, ROC; Department of Medical Informatics, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien City, Hualien County 97004, Taiwan, ROC.
Comput Biol Med. 2015 Oct 1;65:114-23. doi: 10.1016/j.compbiomed.2015.07.022. Epub 2015 Aug 8.
An improved point correspondence method was developed for automatically detecting two-dimensional cephalometric landmarks. The proposed method uses a two-stage rectified point transform: the global correspondence of interest points between two images and the local correspondence of landmarks.
In the first stage, point-to-point matching pairs were established using local corner point features. The matched points on an input image were treated as a set of transformations, with varying directions and magnitudes, from the template image. Similarity of the transformation vectors was achieved through rectification to exclude vectors that deviated widely from the statistical mean. Rectification attempted to remove noise and irrelevant matched points. In the second stage, the point correspondences were fine-tuned within the regional centers of the landmarks, which were classified into three categories-corners, edges, and structural points-and each category was fine-tuned using a different strategy. Correspondence was performed by evaluating the shortest Euclidean distance between the point descriptors of the template and test images.
The correspondence results of 20 orthodontic landmarks were compared with those identified by dental professionals on 80 digital cephalograms collected from a dental clinic. The proposed method detected both hard and soft tissue landmarks with mean error distances of 1.63mm, compared with the 2-mm standard reported by previous studies.
This study enhanced the point correspondence technique for cephalometric landmarking. Using the proposed method, users can preferentially and flexibly add and remove landmarks on a template before correspondence without intensive image pretraining.
开发了一种改进的点对应方法，用于自动检测二维头影测量标志点。所提出的方法使用两阶段校正点变换：两幅图像之间兴趣点的全局对应以及标志点的局部对应。
在第一阶段，使用局部角点特征建立点对点匹配对。输入图像上的匹配点被视为从模板图像进行的一组具有不同方向和大小的变换。通过校正实现变换向量的相似性，以排除与统计均值偏差较大的向量。校正试图去除噪声和不相关的匹配点。在第二阶段，在标志点的区域中心内对点点对应进行微调，这些标志点分为三类——角点、边缘点和结构点——并且每一类使用不同的策略进行微调。通过评估模板图像和测试图像的点描述符之间的最短欧几里得距离来进行对应。
将20个正畸标志点的对应结果与牙科专业人员在从一家牙科诊所收集的80张数字化头影测量片上识别的结果进行比较。与先前研究报告的2毫米标准相比，所提出的方法检测硬组织和软组织标志点的平均误差距离为1.63毫米。
本研究增强了用于头影测量标志点定位的点对应技术。使用所提出的方法，用户在进行对应之前可以在模板上优先且灵活地添加和移除标志点，而无需进行密集的图像预训练。
