Online rebinning is an important and well-established technique for reducing the time required to process PET data. However, the need for efficient data processing in a clinical setting is growing rapidly and is begin...
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Online rebinning is an important and well-established technique for reducing the time required to process PET data. However, the need for efficient data processing in a clinical setting is growing rapidly and is beginning to exceed the capability of traditional online processing methods. High-count rate applications such as rubidium 3-D PET studies can easily saturate current online rebinning technology. Real-time processing at these high count rates is essential to avoid significant data loss. In addition, the emergence of time-of-flight (TOF) scanners is producing very largedata sets for processing. TOF applications require efficient online rebinning methods so as to maintain high patient throughput. Currently, several new hardware architectures like graphics Processing Units (GPUs) are available to speed up the dataparallel and number crunching algorithms. In comparison to the usual parallel systems, such as multiprocessor or clustered machines, GPU hardware can be much faster and above all, it is significantly cheaper. The GPUs have been primarily delivered for graphics for video games but are now being used for high performance computing across many domains. In this study, we investigate the suitability of the GPU for PET rebinning algorithms and also implement them on the same.
Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous s...
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Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to largedatasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.
Do court cases differ from place to place? What kind of picture do we get by looking at a country's collection of law cases? We introduce parallel tag clouds: a new way to visualize differences amongst facets of v...
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Do court cases differ from place to place? What kind of picture do we get by looking at a country's collection of law cases? We introduce parallel tag clouds: a new way to visualize differences amongst facets of very large metadata-rich text corpora. We have pointed parallel tag clouds at a collection of over 600,000 US Circuit Court decisions spanning a period of 50 years and have discovered regional as well as linguistic differences between courts. The visualization technique combines graphical elements from parallel coordinates and traditional tag clouds to provide rich overviews of a document collection while acting as an entry point for exploration of individual texts. We augment basic parallel tag clouds with a details-in-context display and an option to visualize changes over a second facet of the data, such as time. We also address text mining challenges such as selecting the best words to visualize, and how to do so in reasonable time periods to maintain interactivity.
A cell-projection parallel volume rendering system for unstructured grid volume data is proposed in this paper. For this system, the modified early ray termination scheme is proposed to prune the cell-projection of in...
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A cell-projection parallel volume rendering system for unstructured grid volume data is proposed in this paper. For this system, the modified early ray termination scheme is proposed to prune the cell-projection of invisible cell. In order to alleviate load imbalance due to view-dependency in scan-conversion and the dynamic behavior of early-ray termination, the authors also implement dynamic load balancing mechanism into their system. In addition, we have inspected the effect of multicore processor on this system. We need careful cosideration on the process assignment to achieve best performance.
One of the key additions to clinical ultrasound (US) systems during the last decade has been the incorporation of three-dimensional (3D) imaging as a native mode. These systems provide interactive volume rendering and...
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One of the key additions to clinical ultrasound (US) systems during the last decade has been the incorporation of three-dimensional (3D) imaging as a native mode. These systems provide interactive volume rendering and display of 3D data. However, interactive volume rendering is challenging due to the large amount of data I/O and computation required. Many rendering techniques have been developed ranging from fast algorithms with lower image quality to sophisticated algorithms with better image quality, e.g., shear-warp, shear-image-order, pre-integration and direct ray casting. In this paper, we discuss pros and cons of each of these rendering algorithms in terms of algorithm complexity and image quality. We also discuss a new rendering algorithm that we have proposed: pre-integrated shear-image-order (PISIO). PISIO algorithm overcomes the image-quality limitations of other fast algorithms and provides a fine balance between computation and image quality. To demonstrate the interactive performance of these algorithms, we implemented them on a programmable multicore processor, i.e., IBM Cell processor. We present the results in this paper.
Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of informat...
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Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge-bundling through splines;(b) visualization of "branched" clusters to reveal the distribution of the data;(c) opacity-based hints to show cluster density;(d) opacity and shading effects to illustrate local line density on the parallel axes;and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.
parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large d...
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parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on largedata is reduced by edge clutter In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order The experiments on several representative datasets demonstrate the effectiveness of our approach.
Particle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle cha...
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Particle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We present two algorithms targeting upcoming, highly parallel multicore desktop systems to enable interactive navigation and exploration of large particle data sets with global illumination effects. Monte Carlo path tracing and texture mapping are used to capture computationally expensive illumination effects such as soft shadows and diffuse interreflection. The first approach is based on precomputation of luminance textures and removes expensive illumination calculations from the interactive rendering pipeline. The second approach is based on dynamic luminance texture generation and decouples interactive rendering from the computation of global illumination effects. These algorithms provide visual cues that enhance the ability to perform analysis and feature detection tasks while interrogating the data at interactive rates. We explore the performance of these algorithms and demonstrate their effectiveness using several largedata sets. (C) 2007 Elsevier Ltd. All rights reserved.
In this paper we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilist...
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In this paper we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.
Common practice in brain research and brain surgery involves the multi-modal acquisition of brain anatomy and brain activation data. These highly complex three-dimensional data have to be displayed simultaneously in o...
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Common practice in brain research and brain surgery involves the multi-modal acquisition of brain anatomy and brain activation data. These highly complex three-dimensional data have to be displayed simultaneously in order to convey spatial relationships. Unique challenges in information and interaction design have to be solved in order to keep the visualization sufficiently complete and uncluttered at the same time. The visualization method presented in this paper addresses these issues by using a hybrid combination of polygonal rendering of brain structures and direct volume rendering of activation data. Advanced rendering techniques including illustrative display styles and ambient occlusion calculations enhance the clarity of the visual output. The presented rendering pipeline produces real-time frame rates and offers a high degree of configurability Newly designed interaction and measurement tools are provided, which enable the user to explore the data at large, but also to inspect specific features closely. We demonstrate the system in the context of a cognitive neurosciences dataset. An initial informal evaluation shows that our visualization method is deemed useful for clinical research.
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