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Volume Dataset (volume + dataset)

Distribution by Scientific Domains

Information Science and Computing	100%

Selected Abstracts

GPU-based interactive visualization framework for ultrasound datasets

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 1 2009
Sukhyun Lim
Abstract Ultrasound imaging is widely used in medical areas. By transmitting ultrasound signals into the human body, their echoed signals can be rendered to represent the shape of internal organs. Although its image quality is inferior to that of CT or MR, ultrasound is widely used for its speed and reasonable cost. Volume rendering techniques provide methods for rendering the 3D volume dataset intuitively. We present a visualization framework for ultrasound datasets that uses programmable graphics hardware. For this, we convert ultrasound coordinates into Cartesian form. In ultrasound datasets, however, since physical storage and representation space is different, we apply different sampling intervals adaptively for each ray. In addition, we exploit multiple filtered datasets in order to reduce noise. By our method, we can determine the adequate filter size without considering the filter size. As a result, our approach enables interactive volume rendering for ultrasound datasets, using a consumer-level PC. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Progressive Simplification of Tetrahedral Meshes Preserving All Isosurface Topologies

COMPUTER GRAPHICS FORUM, Issue 3 2003
Yi-Jen Chiang
In this paper, we propose a novel technique for constructing multiple levels of a tetrahedral volume dataset whilepreserving the topologies of all isosurfaces embedded in the data. Our simplification technique has two majorphases. In the segmentation phase, we segment the volume data into topological-equivalence regions, that is, thesub-volumes within each of which all isosurfaces have the same topology. In the simplification phase, we simplifyeach topological-equivalence region independently, one by one, by collapsing edges from the smallest to the largesterrors (within the user-specified error tolerance, for a given error metrics), and ensure that we do not collapseedges that may cause an isosurface-topology change. We also avoid creating a tetrahedral cell of negative volume(i.e., avoid the fold-over problem). In this way, we guarantee to preserve all isosurface topologies in the entiresimplification process, with a controlled geometric error bound. Our method also involves several additionalnovel ideas, including using the Morse theory and the implicit fully augmented contour tree, identifying typesof edges that are not allowed to be collapsed, and developing efficient techniques to avoid many unnecessary orexpensive checkings, all in an integrated manner. The experiments show that all the resulting isosurfaces preservethe topologies, and have good accuracies in their geometric shapes. Moreover, we obtain nice data-reductionrates, with competitively fast running times. [source]

Fast Volume Rendering and Data Classification Using Multiresolution in Min-Max Octrees

COMPUTER GRAPHICS FORUM, Issue 3 2000
Feng Dong
Large-sized volume datasets have recently become commonplace and users are now demanding that volume-rendering techniques to visualise such data provide acceptable results on relatively modest computing platforms. The widespread use of the Internet for the transmission and/or rendering of volume data is also exerting increasing demands on software providers. Multiresolution can address these issues in an elegant way. One of the fastest volume-rendering alrogithms is that proposed by Lacroute & Levoy 1 , which is based on shear-warp factorisation and min-max octrees (MMOs). Unfortunately, since an MMO captures only a single resolution of a volume dataset, this method is unsuitable for rendering datasets in a multiresolution form. This paper adapts the above algorithm to multiresolution volume rendering to enable near-real-time interaction to take place on a standard PC. It also permits the user to modify classification functions and/or resolution during rendering with no significant loss of rendering speed. A newly-developed data structure based on the MMO is employed, the multiresolution min-max octree, M 3 O, which captures the spatial coherence for datasets at all resolutions. Speed is enhanced by the use of multiresolution opacity transfer functions for rapidly determining and discarding transparent dataset regions. Some experimental results on sample volume datasets are presented. [source]