Image Segmentation And 3D Reconstruction Technical Research Based On Virtual Human

3D reconstruction and segmentation from Virtual Human Images is a multi-disciplinary subject. It is an important application of computer graphics and image processing in biomedical engineering. It relates to the subjects of digital image processing, computer graphics and some pertinent knowledge of medicine. 3D reconstruction and visualization of Virtual Human Images are widely used in diagnostic, surgery planning and simulating, plastic and artificial limb surgery, radiotherapy planning, and teaching in anatomy. Thus, study on 3D reconstruction and segmentation from Virtual Human Images has important significance on science and worthiness in practical application.The main research contents, of 3D reconstruction and segmentation from Virtual Human Images include the steps of data preparation, such as cut, color change and registration, segmenting and extracting tissues or organs of body, constructing 3D surface models. In this thesis, key techniques for 3D reconstructing from medical images are studied, and a 3D reconstruction system is developed. Virtual Human Images Segmenting and 3D reconstruction base on the steps of data preparation. Using the registration points of images, we cut the image into 600x600, which can greatly decrease the computation cost; By Lap Color space transformation, we smooth the color between different images and separate the red-channel; meanwhile, we use the algorithm of CenteredRigid2DTransform which is supported by ITK to register the virtual Human Images. The results show that the precision and efficiency in the process of image disposal are improved.Segmenting and extracting tissues or organs from medical images are premises of accurate 3D reconstruction models. Considering the features of virtual human images, the area segmentation, contour tracking and Canny operator based Level set algorithm are presented. The Canny operator based Level set algorithm is combined with the advantages of Canny operator which can orient accurately the boundary and the idea that Level set method can evaluative the boundary in image defined space continuously. And it is successfully used in segmenting virtual human images. Marching cubes (MC) algorithm is a classical algorithm to extract iso-surface from regular volume data. This thesis has implemented this algorithm to extract iso-surface from the segmented regions.