Combined Layer-by-Layer Segmentation Level Set Model Applied to Dental CBCT Image Segmentation

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Cone beam computed tomography (CBCT) image technology is widely used in the oral healthcare service industry to Detect the alignment, shape, position, and orientation of the patient's teeth, and tooth segmentation based on the level set model is an important step in the reconstruction and visualization of the tooth's three-dimensional structure. In response to the error accumulation problem that occurs in the conventional layer-by-layer segmentation level set model, this paper proposes a combined layer-by-layer segmentation level set model, which reduces the number of segmentation error transfers, and at the same time reduces the amount of segmentation error accumulation. Two consecutive sets of level set function iterations are designed to lead the curve from the inside of the tooth to the tooth edge and perform accurate tooth contour segmentation. The first set moves the curve from the inside of the tooth to the vicinity of the tooth contour to avoid over-segmentation, and the second set moves the curve from the vicinity of the tooth contour to the edge of the tooth contour for further optimization. Ten CBCT images were randomly selected to test this model, and the experiments showed that the combined layer-by-layer segmentation level-set model was superior to the conventional layer-by-layer segmentation level-set model, with the VOE value reduced by 12.44%, and the results of tooth segmentation were more accurate and better displayed; the time spent was shorter, but not significant; and good segmentation accuracy could be achieved for various classes of teeth.