Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs

BACKGROUND AND OBJECTIVE

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.

METHOD

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.

RESULTS

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.

CONCLUSION

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.

Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model

BACKGROUND AND OBJECTIVE

Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone-loss (ABL) measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an effective method for ABL area localization and denote it as ABLIfBm.

METHOD

ABLIfBm is a threshold segmentation method that uses a hybrid feature fused of both intensity and texture measured by the H-value of fractional Brownian motion (fBm) model, where the H-value is the Hurst coefficient in the expectation function of a fBm curve (intensity change) and is directly related to the value of fractal dimension. Adopting leave-one-out cross validation training and testing mechanism, ABLIfBm trains weights for both features using Bayesian classifier and transforms the radiograph image into a feature image obtained from a weighted average of both features. Finally, by Otsu's thresholding, it segments the feature image into normal and bone-loss regions.

RESULTS

Experimental results on 31 periodontitis radiograph images in terms of mean true positive fraction and false positive fraction are about 92.5% and 14.0%, respectively, where the ground truth is provided by a dentist. The results also demonstrate that ABLIfBm outperforms (a) the threshold segmentation method using either feature alone or a weighted average of the same two features but with weights trained differently; (b) a level set segmentation method presented earlier in literature; and (c) segmentation methods based on Bayesian, K-NN, or SVM classifier using the same two features.

CONCLUSION

Our results suggest that the proposed method can effectively localize alveolar bone-loss areas in periodontitis radiograph images and hence would be useful for dentists in evaluating degree of bone-loss for periodontitis patients.