This paper introduces an interactive classification technique for volume data, called ISpace, which uses Independent Component Analysis (ICA) and a multidimensional histogram of the volume data in a transformed space. Essentially, classification in the volume domain becomes equivalent to interactive clipping in the ICA space, which as demonstrated using several examples is more intuitive and direct for the user to classify data. The result is an opacity transfer function defined for rendering multivariate scalar volume data.

To seek a low-cost, extensible solution for the large-scale data visualization problem, a visual computing system is designed as a result of a collaboration between industry and government research laboratories in Japan, also with participation by researchers in U.S. This scalable system is a commodity PC cluster equipped with the VolumePro 500 volume graphics cards and a specially designed image compositing hardware. Our performance study shows such a system is capable of interactive rendering 5123 and 10243 volume data and highly scalable. In particular, with such a system, simulation and visualization can be performed concurrently which allows scientists to monitor and tune their simulations on the fly. In this paper, both the system and hardware designs are presented.

This is an elementary research into assigning color values to voxels of multi-channel magnetic resonance imaging (MRI) volume data. The MRI volume data sets obtained under different scanning conditions are transformed into components by independent component analysis (ICA), which enhances the physical characteristics of the tissue. The transfer functions for generating color values from the independent components are obtained by using a radial basis function network, a kind of neural net, by training the network with sample data chosen from the Visible Human female data set (VHF). The resultant color volume data sets correspond well with the full-color cross-sections of the Visible Human data sets.

This is a basic study on the visual simulation of neuronal activities using the Hodgkin-Huxley equation (1952). By employing parallel-distributed processing and the cellular automaton concept, this study seeks to reveal the complicated interaction between nerve cells both from microscopic and macroscopic views.

This is basic research for assigning color values to voxels of multichannel MRI volume data. The MRI volume data sets obtained under different scanning conditions are transformed into their components by independent component analysis (ICA), which enhances the physical characteristics of the tissue. The transfer functions for generating color values from independent components are obtained using a radial basis function network, a kind of neural net, by training the network with sample data chosen from the Visible Female data set. The resultant color volume data sets correspond well with the full-color cross-sections of the Visible Human data sets.

The paper presents a voxel-based animation technique which employs either a mass-spring model or a finite element model. Two volumetric objects, a voxelized chair and a scanned muscle, are used as case studies with the two different models. The mass-spring model is used to show an animation sequence of a falling and bouncing chair: wireframe display and volume rendering are used to display a real-time animation of the process. In addition, a real-time simulation is carried out by the finite element method (FEM) of a voxel-based multi-resolution muscle mesh. Two techniques, a direct integration and a simplified modal analysis method are discussed in the context of applying FEM for muscle deformation. Local deformation optimization with modal analysis for higher resolution muscle volumetric animation, which allows accurate prediction of muscle deformation changes, has been used. Physiological muscle force has been considered and a biomechanically-based 3D FEM muscle model has been implemented. Realistic animations have been produced based on the FEM simulation with various graphics techniques.

Virtual colonoscopy is a non-invasive computerized medical procedure for examining the entire colon to detect polyps.We present an interactive virtual colonoscopy method, which uses a physicallybased camera control model and a hardware-assisted visibility algorithm.By employing a potential field and rigid body dynamics, our camera control supplies a convenient and intuitive mechanism for examining the colonic surface while avoiding collisions.Our Zbuffer-assisted visibility algorithm culls invisible regions based on their visibility through a chain of portals, thus providing interactive rendering speed.We demonstrate our method with experimental results on a plastic pipe phantom, the Visible Human, and several patients.

3D virtual colonoscopy has recently been proposed as a non-invasive alternative procedure for the visualization of the human colon. Surface rendering is sufficient for implementing such a procedure to obtain an overview of the interior surface of the colon at interactive rendering speeds. Unfortunately, physicians can not use it to explore tissues beneath the surface to differentiate between benign and malignant structures. In this paper, we present a direct volume rendering approach based on perspective ray casting, as a supplement to the surface navigation. To accelerate the rendering speed, surface-assistant techniques are used to adapt the resampling rates by skipping the empty space inside the colon. In addition, a parallel version of the algorithm has been implemented on a shared-memory multiprocessing architecture. Experiments have been conducted on both simulation and patient data sets.

This article presents a method for decomposing volume data into 3D DoG (difference of Gaussians) functions by using the frame theory of nonorthogonal wavelets. Since we can think of a DoG function as a pair of Gaussian functions, we can consider this method an automatic generation of Blinn's blobby objects (1982). We can also use this representation method for data compression by neglecting the insignificant coefficients, since the wavelet coefficients have significant values only where the volume density changes. Further, since the DoG function closely approximates a /spl nabla//sup 2/G (Laplacian of Gaussian) function, the representation can be considered a hierarchy of the 3D edges on different resolution spaces. Using the spherically symmetric feature of the 3D DoG function, we can easily visualize the 3D edge structure by the density reprojection method. We apply our representation method to medical CT volume data and show its efficiency in describing the spatial structure of the volume.>

The authors reconstruct three dimensional surfaces described by contours on topographic maps. When the elevations of the contours are available, this is an easy problem to solve. However, the authors do not have these elevations and can use only sparsely distributed elevation values noted on the map. This is a difficult problem even for humans to solve. They have developed a method for reconstructing three dimensional surfaces using only contours without matching elevations and these sparsely noted elevation values on the map as input. Numerals and symbols are recognized by the multitangled parallelism (MAP) matching method, while small dots and lines are extracted by the MAP operation method. These results are then used to used to determine the value, position, and attributes of the elevations marked on the topographic maps. The authors define an energy function for reconstruction of the surface. The function integrates three kinds of constraints: smoothness, fitness, and contour. The energy function can be minimized by solving a large linear system of simultaneous equations. The experimental result is a surface reconstructed from a 25,000:1 scale topographic map of the Tsukuba area.>

A method is presented to obtain a unique shape description of an object by using wavelet transforms. Wavelet transform is a signal analysis technique which decomposes a signal using a family of functions having a local property in both time and frequency domains. A multiresolution expression of 3D volume data was first obtained by applying 3D orthogonal wavelet transforms, with the shape then being approximated with a relatively small number of 3D orthogonal functions using only the significant functions. In addition, the resolution of the approximation can be varied point by point using the local property of the wavelets. The method is applied to real volume data, i.e. facial range data and MR images of a human head, and typical results are shown.>

Recently in the field of computer vision, there have been many attempts to obtain a symbolic shape description of an object by fitting simple primitives to the range data of the object. In this paper, we introduce the "Blobby Model" for automatically generating a shape description from range data. This model can express a 3D surface as an isosurface of a scalar field which is produced by a number of field generating primitives. The fields from many primitives are blended with each other and can form a very complicated shape. To determine the number and distribution of primitives required to adequately represent a complex 3D surface, an energy function is minimized which measures the shape difference between the range data and the "Blobby Model". We start with a single primitive and introduce more primitives by splitting each primitive into two further primitives so as to reduce the energy value. In this manner, the shape of the 3D object is slowly recovered as the isosurface produced by many primitives. We have successfully applied this method to human face range data and typical results are shown. The method herein does not require any prior range segmentation.