Surface Rendering Of Arbitrary Section Planes Based On Brain Volume Data Of Virtual Chinese Human(Male No.1)

Digital virtual human is a research field related to a lot of discipline. We gain the two dimensional image sequences by cutting the human sample. So we obtain the research materials: CT, MRI and mechanical cutting. And the data sets of three modalities were obtained by above methods. One of the basic methods to display volume data is to display arbitrary oblique. So we can see the inner tissues. Studies on three dimensional visualization of digital virtual human using the data sets have great significance on research and application.In the reconstruction of the 3D models in this paper, we gain the 3D brain models used triangles from MC algorithm. The we realize the display of arbitrary oblique based on the 3D models of visible human brain.In order to analyze section of the reconstructed tissues and observe the size and structure of inner tissues in multi-tissue reconstruction and visualization, method of cutting the reconstructed 3D models is proposed. The 3D model is cut by a plane or a curved surface. Lists of edges and vertexes in every cut plane are established. Above it, the boundary contour is created and the region enclosed by it is triangulated. The three dimensional matrix algorithm this paper discussed is an improvement to the traditional two dimensional matrix algorithm. And it has the advantage of real-time and high resolution displaying. It uses logical and numerical operating algorithm based on the matrix operation to process the medical images. The ratio of data is high and we can also gain clear images. And we need not to compress the raw data to gain fast speed and low quality images. We gain the section planes and curved surface of the 3D brain models used the above method.In this paper, we realize different kinds of section plane surface rendering in the same environment. And we contrast and analyze the results in detail. It proves that the section plane surface rendering algorithm based on volume data is superior to other algorithm.