Interactive deformation and visualization of level set surfaces using graphics hardware

technical reportDeformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization for applications such as segmentation, surface process- ing, and surface reconstruction. Their usefulness has been limited, however, by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters that can be difficult to tune correctly for specific applications. The second problem is compounded by the first. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates. Our efficient GPU- based solution relies on packing the level-set isosurface data into a dynamic, sparse texture format. As the level set moves, this sparse data structure is updated via a novel GPU to CPU message passing scheme. When the level-set solver is integrated with a real-time volume renderer operating on the same p

A GPU-based, three-dimensional level set solver with curvature flow

technical reportLevel set methods are a powerful tool for implicitly representing deformable surfaces. Since their inception, these techniques have been used to solve prob- lems in fields as varied as computer vision, scientific visualization, computer graphics and computational physics. With the power and flexibility of this approach; however, comes a large computational burden. In the level set ap- proach, surface motion is computed via a partial differential equation (PDE) framework. One possibility for accelerating level-set based applications is to map the solver kernel onto a commodity graphics processing unit (GPU). GPUs are parallel, vector computers whose power is currently increasing at a faster rate than that of CPUs. in this work, we demonstrate a GPU-based, three- dimensional level set solver that is capable of computing curvature flow as well as other speed terms. Results are shown for this solver segmenting the brain surface from an MRI data set

Interactive, GPU-based level sets for 3D brain tumor segmentation

technical reportWhile level sets have demonstrated a great potential for 3D medical image seg- mentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper presents a tool for 3D segmenta- tion that relies on level-set surface models computed at interactive rates on commodity graphics cards (GPUs). The mapping of a level-set solver to a GPU relies on a novel mechanism for GPU memory management. The interactive rates for solving the level- set PDE give the user immediate feedback on the parameter settings, and thus users can tune three separate parameters and control the shape of the model in real time. We have found that this interactivity enables users to produce good, reliable segmen- tations. To support this observation, this paper presents qualitative and quantitative results from a study of brain tumor segmentation

Interactive deformation and visualization of level set surfaces using graphics hardware

Journal ArticleDeformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization for applications such as segmentation, surface processing, and surface reconstruction. Their usefulness has been limited, however, by their high computational cost and and reliance on significant parameter tuning. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing levelset solutions at interactive rates. Our efficient GPU-based solution relies on packing the level-set isosurface data into a dynamic, sparse texture format. As the level set moves, this sparse data structure is updated via a novel GPU to CPU message passing scheme. When the level-set solver is integrated with a real-time volume renderer operating on the same packed format, a user can visualize and steer the deformable level-set surface as it evolves. In addition, the resulting isosurface can serve as a region-of-interest specifier for the volume renderer. This paper demonstrates the capabilities of this technology for interactive volume visualization and segmentation

Streaming narrow-band algorithm: interactive computation and visualization of level sets

Journal ArticleAbstract-Deformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization and computer graphics for applications such as segmentation, surface processing, and physically-based modeling. Their usefulness has been limited, however, by their high computational cost and reliance on significant parameter tuning. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates. The proposed solution is based on a new, streaming implementation of the narrow-band algorithm. The new algorithm packs the level-set isosurface data into 2D texture memory via a multidimensional virtual memory system. As the level set moves, this texturebased representation is dynamically updated via a novel GPU-to-CPU message passing scheme. By integrating the level-set solver with a real-time volume renderer, a user can visualize and intuitively steer the level-set surface as it evolves. We demonstrate the capabilities of this technology for interactive volume segmentation and visualization

Level set and PDE methods for visualization

Notes from IEEE Visualization 2005 Course #6, Minneapolis, MN, October 25, 2005. Retrieved 3/16/2006 from http://www.cs.drexel.edu/~david/Papers/Viz05_Course6_Notes.pdf.Level set methods, an important class of partial differential equation (PDE) methods, define dynamic surfaces implicitly as the level set (isosurface) of a sampled, evolving nD function. This course is targeted for researchers interested in learning about level set and other PDE-based methods, and their application to visualization. The course material will be presented by several of the recognized experts in the field, and will include introductory concepts, practical considerations and extensive details on a variety of level set/PDE applications. The course will begin with preparatory material that introduces the concept of using partial differential equations to solve problems in visualization. This will include the structure and behavior of several different types of differential equations, e.g. the level set, heat and reaction-diffusion equations, as well as a general approach to developing PDE-based applications. The second stage of the course will describe the numerical methods and algorithms needed to implement the mathematics and methods presented in the first stage, including information on implementing the algorithms on GPUs. Throughout the course the technical material will be tied to applications, e.g. image processing, geometric modeling, dataset segmentation, model processing, surface reconstruction, anisotropic geometric diffusion, flow field post-processing and vector visualization. Prerequisites: Knowledge of calculus, linear algebra, computer graphics, visualization, geometric modeling and computer vision. Some familiarity with differential geometry, differential equations, numerical computing and image processing is strongly recommended, but not required

Interactive Computation and Visualization of Level-Set Surfaces: A Streaming Narrow Band Algorithm

Deformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization and computer graphics for applications such as segmentation, surface processing, and surface reconstruction. Their usefulness has been limited, however, by two problems. First, three-dimensional level sets are relatively slow to compute. Second, their formulation usually entails free parameters that can be difficult to tune correctly for specific applications. The second problem is compounded by the first. This thesis presents a solution to these challenges by describing graphics processor unit (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates for volumes as large as 256 x 256 x 256. Level-set techniques deform isosurfaces by solving partial differential equations (PDEs) on a voxel grid. Efficient solvers for the equations compute a solution only at those voxels on or near the isosurface. The active elements in this narrow-band of computation change as the level-set solution evolves. This thesis demonstrates that such dynamic sparse-grid computations can be efficiently solved using a streaming architecture platform--a modern graphics processor. The solution uses a multidimensional virtual memory mapping to pack the active, three-dimensional voxel data into two-dimensional texture memory on the GPU. A novel GPU-to-CPU message passing scheme quickly updates this sparse data structure as the isosurface moves. The integration of the level-set solver with a real-time volume renderer allows a user to visualize and steer the deformable level-set surface as it evolves. The resulting isosurface can also serve as a region-of-interest specifier for the volume renderer. This thesis demonstrates the capabilities of this technology for interactive volume segmentation and visualization. This thesis also presents an evaluation of the method with a brain tumor segmentation user study

Glift: Generic Data Structures for Graphics Hardware

This thesis presents Glift, an abstraction and generic template library for parallel, random-access data structures on graphics hardware. We demonstrate that a data structure abstraction for graphics processing units (GPUs) can simplify the description of new and existing data structures, stimulate development of complex GPU algorithms, and perform equivalently to hand-coded implementations. Glift defines the GPGPU computation model in terms of parallel iteration over data structure elements and demonstrates iteration over complex structures. This thesis also presents a case that future interactive rendering solutions will be an inseparable mix of general-purpose, parallel GPU programming (GPGPU) and traditional graphics programming. We describe the use of Glift in four novel interactive rendering algorithms with complex data structure and iterator requirements: octree 3D paint, adaptive shadow maps, resolution-matched shadow maps and a heat-diffusion depth-of-field algorithm