Image Segmentation of Tooth and Alveolar Bone with the Level Set Model

Segmentation of tooth and alveolar bone from the cone beam computed tomography (CBCT) images provides the basic data for the three-dimensional reconstruction and visualization of bone structure. In this paper, according to the characteristics of tooth and alveolar bone, an improved potential well function was combined with the level set model for segmentation of tooth and alveolar bone, overcoming the defects of 'stop evolution' or 'too fast evolution' that might occur with the use of conventional potential well functions. Since it is difficult to effectively filter out the noises in CBCT image with the single variance Gaussian filter, a multiple small variance Gaussian filter stack was used to preprocess the image. As the contours of the same tooth in adjacent images of the image sequence showed only little changes, the segmentation result of the current layer was taken as the initial contour of the curve evolution for the next layer to reduce the times of iteration and increase the speed of segmentation. In addition, the algorithm is also used to segment a single tooth in magnetic resonance image of oral cavity successfully