For tooth segmentation problem on the three-dimensional computed tomography (CT) volume data, this paper proposes a regional adaptive deformable model for tooth structure measurement of CT images. The proposed method combines the automatic thresholding segmentation, CV active contour model, and graph-cut. Firstly, we achieved the segmentation and location of dental crowns by automatic thresholding segmentation. And then by using the above segmentation result as the initial contour, we utilized active contour method to slice gradually the segment of remaining tooth. By incorporating active contour and graph-cut then, we realized the accurate segmentation for tooth root, which is the most difficult to be segmented. The experimental results showed that the proposed tooth structure measurement accurately and automatically segmented dental crowns from CT data, and then rapidly and accurately segmented the tooth neck and tooth root. The structure of tooth could be effectively segmented from CT data by using the proposed method. Experimental results indicated that the proposed method was rather robust and accurate, and could effectively assist the doctor for diagnosis in clinical treatment.
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.