Hybrid level set based three-dimensional tooth modeling method

Abstract

The invention provides a hybrid level set based three-dimensional tooth modeling method. The method comprises the following multiple steps: 1) selecting an initial slice and initializing a level set; 2) segmenting a tooth root slice image by utilizing a single-phase hybrid level set model constructed by combining priori shape constraint energy, Flux model based edge energy and priori gray based local region energy; 3) segmenting a tooth crown layer slice image by a dual-phase hybrid level set model in combination with regional competition constraint; and 4) reconstructing a contour segmentation based three-dimensional tooth model. According to the method, the hybrid level set model can effectively solve the problems of inaccuracy for edge positioning, incapability of processing an image, uneven gray level and the like in a conventional segmentation model, and an independent three-dimensional model of each tooth can be quickly and accurately constructed, so that a solid foundation is laid for making oral repair plans, biomechanical analysis and the like.

Description

technical field [0001] The invention belongs to the field of image processing and relates to the application of a level set method in three-dimensional tooth modeling. Background technique [0002] In recent years, with the rapid development of three-dimensional digital imaging technology in the field of stomatology, more and more computer-aided diagnosis and treatment have been applied in oral restoration, and it has gradually become the development trend in this field. In the computer oral restoration system, the first important thing is to obtain a digital three-dimensional tooth model, and the accuracy and integrity of the model are directly related to the results of subsequent tooth arrangement, implantation, orthodontics, and biomechanical analysis. [0003] At present, the most commonly used method for tooth modeling is to use image processing technology to segment tooth CT image sequences, that is, firstly segment tooth contours from each layer of CT slices, and then...

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Problems solved by technology

) Use binarization and boundary extraction to screen out the key points of the tooth contour of each slice, and then use the 3D-Delaunay tetrahedronization algorithm to obtain the solid model of the entire tooth; but the binarization operation is prone to over-segmentation or under-segmentation problems

Wu et al. (X.Wu, H.Gao, H.Heo, et al.ImprovedB-splinecontourfittingusinggeneticalgorithmforthesegmentationofdentalcomputerizedtomographyimagesequences.TheJournalofimagingscienceandtechnology, 2007,51(4):328-336.) use B-spline curve fitting based on genetic algorithm to extract each layer Sliced ​​tooth contours, but B-splines cannot handle tooth topology changes

Gao et al. (H.Gao, O.Chae.IndividualtoothsegmentationfromCTimagesusinglevelsetmethodwithshapeandintensityprior.PatternRecognition, 2010, 43: 2406-2417) used a level set-based active contour model for tooth segmentation, and used different segmentation models for different tooth layer slices, which can separate each The crown and root of each tooth are segmented; the model mainly relies on the image edge gradient and the probability distribution of the prior shape to guide the evolution of the level set, but because the tooth density is not uniform, and the surrounding is easily disturbed by structures such as alveolar bone , so relying on the gray-level probability of the surrounding area of ​​the prior shape to control the shrinkage and expansion of the level set contour is prone to the problem of boundary leakage

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