This paper presents an efficient graphics hardware-based method to segment and visualize level-set surfaces as
interactive rates. Our method is composed of page manager, level-set solver, and volume renderer. The page manager
which performs in CPU generates page table, inverse page table and available page stack as well as processes the
activation and inactivation of pages. The level-set solver computes only voxels near the iso-surface. To run efficiently
on GPUs, volume is decomposed into a set of small pages. Only those pages with non-zero derivatives are stored on
GPU. These active pages are packed into a large 2D texture memory. The level-set partial differential equation (PDE) is
computed directly on this packed format. The page manager is used to help managing the packing of the active data.
The volume renderer performs volume rendering of the original data simultaneously with the evolving level set in GPU.
Experimental results using two chest CT datasets show that our graphics hardware-based level-set method is much
faster than software-based one.
We propose a fast 2D-3D marker-based registration technique to fuse anatomical structure of 3D CT scans onto 2D X-ray fluoroscopy image. Our method is composed of three stages. First, DRRs (Digitally Reconstructed Radiography) are generated by maximum intensity projection based on hardware texture-based volume rendering. This technique is over 200 times faster than software-based one. Second, confirmation markers are automatically segmented in DRRs and X-ray fluoroscopy images, respectively. Third, in/out-plane registration is proposed for real-time performance. In out-plane registration, we search for an optimal position of X-ray source in a 3D spherical coordinate system. Then we calculate optimal translation and rotation vectors by using principal axes method in in-plane registration. Our method has been successfully six different CT and X-ray fluoroscopy pairs generated from cardiac phantom datasets. For accuracy evaluation, we calculate root-mean-squared error (RMSE) between confirmation markers of DRRs and X-ray fluoroscopy images. Experimental results show that our DRRs generation method performs very fast and the hierarchical registration effectively finds the matching of DRRs and 2D images.