Research On 3D Modeling Technology Based On CT Scanning Data

With the improvement of the computer operating ability and the development of medical image technology, it makes great changes in disease diagnosis and treatment. CT date aided medical diagnosis and treatment can improve the efficiency and success rate of the surgery largely, which is the research focus of digital medical nowadays. Due to the shortcomings of the traditional CT data boundary-edge extraction and reconstruction. A new contour segmentation method of DICOM data is researched. At the same time, we researched the new methods to reconstruct the point cloud data and how to remove the self-intersection more quickly.The main research contents are as follows:(1) The paper researchs a contour automatic extracting method for data segmentation based on shrinking algorithm in orthopedics CT images. First of all,we detect all the boundray pixels with Laplace operator and overlay the boundray pixels with the original image. Secondly, the contour registration mapping technology passes the outline down heuristically. Ultimately,the Shrink-wrapping algorithm is used to segment the image sequences automatically. In this paper, we did some boundary-edge extraction experiments. The experimental results verify the effectiveness and efficiency of the algorithm.(2) Comparing two kinds of 3D reconstruction algorithms that are respectively based on the direct volume rendering and surface triangularization. We designs a 3D reconstruction algorithm based on triangle grid subdivision surfacing in the paper. Firstly, it is essential to filter each layer of noisy point cloud data. We delete cloud data that deviate from the discrete point center coordinate and then construct triangles on the neighboring border point cloud data. Finally triangle grid surfacing boundary subdivision algorithm is used in the paper. Experimental results show that the triangle grid subdivision surfacing modeling of 3D reconstruction method can improve the stability and efficiently in the 3D reconstruction process.(3) Removing self-intersection is realized based on straight line intersection algorithm. In the beginning, we offset model with certain thickness based on point offset algorithm and the point coordinates on the original model are applied as the projection line original points. It is essential to build straight lines vector by calculating the average of adjacent plane vector.The projection straight lines get through the offseted model. We push the intersecting triangles into the lists. At last, we filter the point cloud data and rebuild the grid model. We did experiments with the different discrete data models. It proves that the algorithm is feasible.The VS2008 software is used as the developing tool and the Open GL 2.0 library function is applied as image rendering platform. The MFC class libraries are applied to build the soft framework. We test the algorithms by using medical CT series data. The experimental results verify that the algorithms can not only have the certain practical value in medical diagnosis but also it makes sense to the improvement of speed and quality in digital medical diagnosis and treatment.