KEYWORDS: Visualization, Lung, Nerve, Volume visualization, 3D image processing, 3D acquisition, 3D modeling, Magnetic resonance imaging, Microscopy, Data modeling
The aim of this work is to visualize 3D objects in volume data with minimum numbers of user-defined model or parameters. In this report, we propose a novel method that utilizes the distances along the optimum paths between a seed voxel in a target object and other voxels. The distance is defined using gradient between adjacent voxels or using difference between the seed voxel and other voxels along the optimum path. The optimization of a path is carried out by selecting the path where the largest value of absolute gradient or difference along a path is minimum, and the distance of each voxel is the largest value along the optimum path. The visualization is performed by rendering the volume where the initial voxel values are replaced with the distances. By the proposed method, the volume visualization can be accomplished only by setting a seed voxel in the target object. From experiments for visualizing human embryos obtained with MR microscopy, we confirmed that the proposed method successfully visualized the objects.
KEYWORDS: 3D modeling, Solid modeling, 3D image processing, Standards development, 3D imaging standards, Magnetic resonance imaging, Natural surfaces, 3D acquisition, 3D displays, Image resolution
Embryology is one of the basic subjects in medical education, to learn the process of human development especially from fertilization to birth. The shape deformation in the development of human embryo is one of the most important points to be comprehended, but it is difficult to illustrate the deformation by texts, 2D drawings, photographs and so on, because it is extremely complicated. The purpose of our research is to construct a 3D model sequence to illustrate the deformation of human embryo, and to make the model sequence into the teaching materials for medical education. Firstly, 3D images of the specimens of human embryo were acquired using MR microscopy. Next, an initial 3D model sequence was manually modified by comparing with the features of the acquired images under the supervision of medical doctors, because the images were influenced not only by the noise or limitation of resolution in MR image acquisition, but also by the variation of shape depending on the difference of subject. Using the constructed 3D model sequence, CG animations and an interactive VRML system were composed as the teaching materials for embryology. These materials were quite helpful to understand the shape deformation compared with the conventional materials.
A new method is proposed for reconstructing the 3D structure of the coronary arterial tree from angiograms. Instead of identification of corresponding points on the images, several sets of biplane angiograms are used, and the parameters of the imaging geometries are simultaneously estimated. Several sets of biplane angiograms are usually obtained during one angiographic test. However, only one set of biplane angiogram is usually used for 3D reconstruction of the coronary arterial tree. If only one set of biplane angiogram is used for 3D reconstruction, it is necessary to identify corresponding points on both images. Identification of correspondent points on both images is, however, very difficult and often impossible. To overcome this difficulty, we use several sets of biplane angiograms for 3D reconstruction. If the precise parameters of the imaging geometries are known, the 3D structure of the coronary arterial tree can be obtained by back parameters of the imaging geometries are known, the 3D structure of the coronary arterial tree can be obtained by back projecting each angiogram. However, only the approximate parameters of the imaging geometries are usually known. Therefore, we developed a method for 3D reconstruction of a coronary arterial tree with simultaneous estimation of the imaging geometry. In this paper, we present the algorithm for our method and demonstrate the application to clinical data.
we propose a novel method that deals with simultaneous process of registration among images and segmentation of image using plural images for 3D head images of different modalities in identical subject. In this method, image segmentation is performed by using the result of vector quantification (VQ) for multi-dimensional feature space distribution that describes the relation of voxel value. Registration is carried out by optimization of parameters of translation and rotation using the minimization of VQ distortion. First, we examined characteristics of feature space histogram using simplified head images. Next, we showed the usefulness of proposed method for these images. Here, we estimated the measure of VQ distortion and automated method to extract VQ centroids. Finally, an example that applied this method to T1 emphasized MR image and cbf-PET image was shown.
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