Tomography-image-based facial skin three-dimensional surface model reconstructing method

Abstract

The invention discloses a tomography-image-based facial skin three-dimensional surface model reconstructing method, and belongs to the field of computer image and graph processing. The method comprises the following steps of: reconstructing a facial skin three-dimensional model based on craniofacial tomography data; converting the facial skin three-dimensional model into a Frankfurt coordinate system; establishing a voxel model of the facial skin three-dimensional model; extracting outside surface vertexes of the facial skin three-dimensional model based on breadth first traversal of a connected graph and multi-viewpoints visibility detection by using the voxel model as input; and creating the facial skin three-dimensional surface model according to all extracted surface vertexes and topological information of the vertexes in the original model file. According to the invention, a novel method for reconstructing a facial skin surface based on tomography images is provided, and the method has the advantages of high automation degree and high result accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer image graphics processing, and in particular relates to a method for reconstructing a three-dimensional surface model of a face skin based on a tomographic image. Background technique [0002] With the appearance of tomographic imaging equipment such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound, people can easily obtain tomographic images of human organs and three-dimensional objects. The image information of the human body or three-dimensional objects at a certain level provided by tomographic images provides reliable information for doctors and engineers to deeply understand the internal tissue structure and morphological details of human organs or three-dimensional objects. 3D model reconstruction based on tomographic images has broad application prospects in medical 3D reconstruction, virtual visual surgery, reverse engineering and other fields. [0003] For ...

AI Technical Summary

Problems solved by technology

There are two main defects in this method: ① The target contour obtained by contour extraction contains every pixel that constitutes it, and the data redundancy is too large, so it must be streamlined

The existing contour reduction algorithms mainly include equidistant sampling method and curvature sampling method. The former will lead to the loss of a large number of feature points, while the latter can retain feature points to a large extent, but because the curvature calculation formula will involve the second derivative Calculation, the calculation process is more complicated; (2) The shape of the skin outer contour extracted from the tomographic image of the skin is complex, and the local shape between two adjacent contour lines is very different, or even completely different, which brings great difficulties to the stitching of adjacent contour lines. The classic contour stitching algorithm, the shortest diagonal method, can only obtain a good reconstruction effect when the upper and lower contour lines are similar in shape, and the number of points is close and uniform at the same time, so it can be directly applied When splicing adjacent contour lines in the tomographic image of the skin, it is easy to generate a large number of obtuse triangles, that is, the pyramid phenomenon, which affects the fidelity and smoothness of the skin generation model, and the improved shortest diagonal method has a certain degree of However, it is still impossible to theoretically guarantee that the vertex connection rule in the reconstructed skin model mesh is optimal, and it cannot guarantee that the shape difference between the reconstructed skin model and the living skin is the smallest.

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