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This paper describes a new multi-scale approach for the extraction of iso-surfaces from volume datasets. The goal is to automatically identify iso-surfaces that best approximate the boundary surfaces at different levels of details. Using histogram analysis, iso-values are extracted from histograms of boundary voxels defined by gradient thresholding or zero-crossing boundaries. Multi-scale smoothing of the histogram using Gaussian filters of various sizes allows the iso-surfaces to be defined hierarchically over a scale space map. It provides an interactive environment and volume navigation tools for the exploration of large volume datasets by visualizing the layers of the volume space in a multi-scale manner.

With the rapid advancement in high-resolution confocal imaging, various forms of microscopy deliver substantial amount of valuable 3D cell biological information. Currently, image processing, modeling, visualization and analysis on confocal microscopic datasets are, however, still more or less following the traditional fashion that is 2D centric via a slice-by-slice strategy. Such image sequence based operations not only leads to lengthy processing times but also can potentially cause problems in data communication and interpretation, and knowledge discovery in a global 3D cellular level. In this paper, we describe our solution for processing, visualization and quantification of 3D confocal images with the CellStudio system we developed. CellStudio has a collaborative feature allowing 3D confocal data to be collected and retrieved across the net. CellStudio is also an integrated solution enabling 3D confocal image processing, volumetric visualization and interactive quantification performed in a network connected PC/Window platform.

Performance improvements to the known opacity correction mechanisms for over-sampled volume ray casting (VRC), especially using two forms of commodity hardware, are explored. Data-parallel strategies that enable exploitation of parallelism using either: (1) a programmable graphics processing unit (GPU) or (2) cluster computation are a prime focus. The GPU-based approach is finely granular. The cluster-based approaches here utilize less finely granular processing that allows acceleration through multi-processing and multi-threading. These approaches also include features, such as early ray termination, empty-space skipping, term rearrangement, and term reduction, that have not been previously explored in depth for opacity-corrected VRC. A new strategy enabling more accurate opacity correction is also presented. The performance of the improvements on real volume data are also explored. The improvements allow opacity correction to be performed in a way that efficiently exploits either GPU- or cluster-based capabilities.

We propose a method of sampling regular and irregular-grid volume data for visualization. The method is based on the Metropolis algorithm that is a type of Monte Carlo technique. Our method enables "importance sampling" of local regions of interest in the visualization by generating sample points intensively in regions where a user-specified transfer function takes the peak values. The generated sample-point distribution is independent of the grid structure of the given volume data. Therefore, our method is applicable to irregular grids as well as regular grids. We demonstrate the effectiveness of our method by applying it to regular cubic grids and irregular tetrahedral grids with adaptive cell sizes. We visualize volume data by projecting the generated sample points onto the 2D image plane. We tested our sampling with three rendering models: an X-ray model, a simple illuminant particle model, and an illuminant particle model with light-attenuation effects. The grid-independency and the efficiency in the parallel processing mean that our method is suitable for visualizing large-scale volume data. The former means that the required number of sample points is proportional to the number of 2D pixels, not the number of 3D voxels. The latter means that our method can be easily accelerated on the multiple-CPU and/or GPU platforms. We also show that our method can work with adaptive space partitioning of volume data, which also enables us to treat large-scale/complex volume data easily.