Three-dimensional Reconstruction Techniques For Medical Image Research

This dissertation mostly studies the technology of three-dimensional reconstruction of medical images. Three-dimensional reconstruction of medical images is a multi-disciplinary subject. It is an important application of computer graphics and image processing in biomedicine engineering. It is related to the subjects of digital image processing, computer graphics and some related knowledge of medical. Three-dimensional reconstruction and visualization of medical images are widely used in diagnostic, surgery planning and simulating, plastic and artificial limb surgery, and teaching in anatomy. Study on three-dimensional reconstruction of medical images has important significance on science and worthiness in practical application.The main research contents of three-dimensional reconstruction of medical images include image pre-processing, such as filtering and interpolating, segmenting and extracting tissues of organs of body, and constructing three-dimensional models. In this dissertation, key techniques for three-dimensional reconstruct of medical images (such as CT, MR images) are studied. A three-dimensional segmentation method of medical volume data is presented by allowing for more sophisticated classification using spatial information. The segmentation process consists of following two steps, the first step is to fill the regions using three-dimensional region growing approach, and the second step is properly using mathematical morphology operation according to the feature of tissues of regions to extract. Using this segmentation method, we successfully extract the liver from the abdomen volume data.Direct volume rendering provides medical users with insight into datasets by creating a three-dimensional representation from a set of two-dimensional image slices. A new method of the direct volume rendering based on the texture-mapping hardware assisted accelerating is synthetically analyzed. The approach uses 2D or 3D textured data slice, and makes the 3D image sing a blending operator. This approach described here is equivalent to ray casting and produces the same results. Unlike ray casting, where each image pixel is built up ray by ray, this approach takes advantage of spatial coherence. The texture-mapping based approach is used as a voxel (volume pixel)...