Lin P L, Huang P Y, Huang P W
Department of Computer Science and Information Engineering, Providence University, 200, Taiwan Bld., Shalu, Taichung 43301, Taiwan.
Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Comput Methods Programs Biomed. 2017 Sep;148:1-11. doi: 10.1016/j.cmpb.2017.06.012. Epub 2017 Jun 24.
Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone loss measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an automatic length-based alveolar bone loss measurement system with emphasis on a cementoenamel junction (CEJ) localization method: CEJ_LG.
The bone loss measurement system first adopts the methods TSLS and ABLifBm, which we presented previously, to extract teeth contours and bone loss areas from periodontitis radiograph images. It then applies the proposed methods to locate the positions of CEJ, alveolar crest (ALC), and apex of tooth root (APEX), respectively. Finally the system computes the ratio of the distance between the positions of CEJ and ALC to the distance between the positions of CEJ and APEX as the degree of bone loss for that tooth. The method CEJ_LG first obtains the gradient of the tooth image then detects the border between the lower enamel and dentin (EDB) from the gradient image. Finally, the method identifies a point on the tooth contour that is horizontally closest to the EDB.
Experimental results on 18 tooth images segmented from 12 periodontitis periapical radiographs, including 8 views of upper-jaw teeth and 10 views of lower-jaw teeth, show that 53% of the localized CEJs are within 3 pixels deviation (∼ 0.15 mm) from the positions marked by dentists and 90% have deviation less than 9 pixels (∼ 0.44 mm). For degree of alveolar bone loss, more than half of the measurements using our system have deviation less than 10% from the ground truth, and all measurements using our system are within 25% deviation from the ground truth.
Our results suggest that the proposed automatic system can effectively estimate degree of horizontal alveolar bone loss in periodontitis radiograph images. We believe that our proposed system, if implemented in routine clinical practice, can serve as a valuable tool for early and accurate diagnosis of alveolar bone loss in periodontal diseases and also for assessing the status of alveolar bone following various types of non surgical and surgical and regenerative therapy. For overall system improvement, a more objective comparison by using transgingival bone measurement with a periodontal probe as the ground truth and enhancing the localization algorithms of these three critical points are the two major tasks.
牙周炎会导致牙齿周围牙槽骨逐渐丧失。因此，在根尖片上自动测量牙槽骨丧失情况可辅助牙医诊断此类疾病。在本文中，我们提出一种基于长度的牙槽骨丧失自动测量系统，重点介绍一种釉牙骨质界（CEJ）定位方法：CEJ_LG。
骨丧失测量系统首先采用我们之前提出的TSLS和ABLifBm方法，从牙周炎X光图像中提取牙齿轮廓和骨丧失区域。然后应用所提出的方法分别定位CEJ、牙槽嵴（ALC）和牙根尖（APEX）的位置。最后，系统计算CEJ与ALC位置之间的距离与CEJ与APEX位置之间的距离之比，作为该牙齿的骨丧失程度。CEJ_LG方法首先获取牙齿图像的梯度，然后从梯度图像中检测釉质和牙本质之间的边界（EDB）。最后，该方法在牙齿轮廓上确定一个水平方向上最接近EDB的点。
对从１２张牙周炎根尖片中分割出的１８张牙齿图像进行实验，其中包括８张上颌牙齿图像和１０张下颌牙齿图像，结果显示，５３％的定位CEJ与牙医标记位置的偏差在３个像素以内（约０.１５毫米），９０％的偏差小于９个像素（约０.４４毫米）。对于牙槽骨丧失程度，使用我们系统进行的测量中，超过一半与真实值的偏差小于１０％，所有测量值与真实值的偏差均在２５％以内。
我们的结果表明，所提出的自动系统能够有效估计牙周炎X光图像中水平牙槽骨丧失的程度。我们相信，我们提出的系统如果应用于常规临床实践，可作为一种有价值的工具，用于早期准确诊断牙周疾病中的牙槽骨丧失情况，也可用于评估各种非手术、手术及再生治疗后的牙槽骨状况。为了全面改进系统，以牙周探针进行龈下骨测量作为真实值进行更客观的比较，以及改进这三个关键点的定位算法是两项主要任务。
