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ieee International symposium on Biomedical Imaging

作者： Kimberly Powell NVIDIA Corporation USA

ISBN: (纸本)9781424439317

The biomedical imaging chain is continuously being challenged to reconstruct, analyze, and visualize increasing amounts of data in shorter amounts of time. parallel computing on multi-core devices and clustered computers has allowed for continued innovation of compute and processing technologies but not without facing serious constraints of cost, space, and power consumption. Over the last three years the graphics processing unit (GPU) and its increased programmability has played an integral role in defining a new dimension to parallel computing with its single chip, many-core architecture as well as evolving the graphics pipeline to enhance visualization techniques. Image reconstruction, segmentation and registration algorithms architected to take advantage of the GPU parallel architecture not only realize massive processing speedups but also set the stage for scalability. High resolution rendering of 3D and 4D datasets are navigated in interactive, real-time approaches. Real time ray tracing and 3D stereoscopic solutions bring increased realism to images. Understanding the optimized mix of GPU and CPU, both in the sense of hardware and software, is necessary for imaging applications to innovate, realize cost/performance efficiency and continue to enhance visualization. Several approaches for GPU programmability are available and will be explored. Innovations in the compute, graphics and visualization space will be discussed to show the relevance of the GPU throughout the imaging chain.

关键词： Biomedical imaging Ecosystems visualization Image reconstruction parallel processing Biomedical computing Concurrent computing Technological innovation Space technology graphics

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SpRay: A visual analytics approach for gene expression data

SpRay: A visual analytics approach for gene expression data

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ieee symposium on Visual Analytics Science and Technology (VAST)

作者： Janko Dietzsch Julian Heinrich Kay Nieselt Dirk Bartz ZBIT University of Tübingen Germany University of Stuttgart Germany ICCAS/VCM Universität Leipzig Germany

We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by micro-array experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications.

关键词： Spraying Visual analytics Gene expression Bioinformatics Statistical analysis Application software Terminology Computer graphics data analysis data visualization

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Visual analysis of graphs with multiple connected components

Visual analysis of graphs with multiple connected components

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ieee symposium on Visual Analytics Science and Technology (VAST)

作者： Tatiana von Landesberger Melanie Gorner Tobias Schreck Interactive Graphics Systems Group Technische Universität Darmstadt and Fraunhofer IGD Germany Interactive Graphics Systems Group Technische Universität Darmstadt Germany

In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the self-organizing-map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder network analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types.

关键词： data visualization graphics Clustering algorithms data analysis Topology Displays data structures Information retrieval Image retrieval User interfaces

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Synchronizing parallel data Streams via Cross-Stream Coding

Synchronizing Parallel Data Streams via Cross-Stream Coding

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Shaofeng Liu Jurgen P. Schulze Tomas A. Defanti California Institute for Telecommunications and Information Technology (Calit2) University of California San Diego (UCSD) United States

Streaming very-high-definition visualization data objects on top of optical networks is critical in many scientific research areas, including video streaming/conferencing, remote rendering on tiled display walls, 3D virtual reality applications, etc. Current data streaming protocols rely on UDP as well as a variety of compression techniques. However, none of the protocols scale well to the parallel streaming model of large scale graphic applications, and the existing parallel streaming protocols have limited synchronization mechanisms to synchronize the streams efficiently, and are prone to be slowed down by just one slow stream. In this paper, we propose a new parallel streaming protocol that can stream synchronized multiple Gbps media content over optical networks through reliable Cross-Stream packet coding, which not only tolerates random UDP packet loss, but also aims to achieve good synchronization performance across multiple parallel data streams with reasonable coding overhead. We simulated the approach, and the results show that our approach can generate steady throughput with fluctuating data streams.

关键词： Streaming media Protocols Optical fiber networks data visualization Rendering (computer graphics) Three dimensional displays Virtual reality large-scale systems graphics Random media

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Structure-aware viewpoint selection for volume visualization

Structure-aware viewpoint selection for volume visualization

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Yubo Tao Hai Lin Hujun Bao Feng Dong Gordon Clapworthy State Key Lab of CAD&CG University of Zhejiang China University of Bedfordshire UK

Viewpoint selection is becoming a useful part in the volume visualization pipeline, as it further improves the efficiency of data understanding by providing representative viewpoints. We present two structure-aware view descriptors, which are the shape view descriptor and the detail view descriptor, to select the optimal viewpoint with the maximum amount of the structural information. These two proposed structure-aware view descriptors are both based on the gradient direction, as the gradient is a well-defined measurement of boundary structures, which have been proved as features of interest in many applications. The shape view descriptor is designed to evaluate the overall orientation of features of interest. For estimating local details, we employ the bilateral filter to construct the shape volume. The bilateral filter is very effective in smoothing local details and preserving strong boundary structures at the same time. Therefore, large-scale global structures are in the shape volume, while small-scale local details still remain in the original volume. The detail view descriptor measures the amount of visible details on boundary structures in terms of variances in the local structure between the shape volume and the original volume. These two view descriptors can be integrated into a viewpoint selection framework, and this framework can emphasize global structures or local details with flexibility tailored to the user's specific situations. We performed experiments on various types of volume datasets. These experiments verify the effectiveness of our proposed view descriptors, and the proposed viewpoint selection framework actually locates the optimal viewpoints that show the maximum amount of the structural information.

关键词： Rendering (computer graphics) Filters Pipelines Smoothing methods data visualization large-scale systems Volume measurement Shape measurement Computer graphics data mining

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Interactive feature extraction and tracking by utilizing region coherency

Interactive feature extraction and tracking by utilizing reg...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Chris Muelder Kwan-Liu Ma University of California Davis USA

The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost.

关键词： Feature extraction data mining data visualization Acceleration Computer graphics Computational modeling Application software large-scale systems Numerical simulation Costs

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Fast and sleek glyph rendering for interactive HARDI data exploration

Fast and sleek glyph rendering for interactive HARDI data ex...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： T.H.J.M. Peeters V. Prckovska M. van Almsick A. Vilanova B.M. ter Haar Romeny

High angular resolution diffusion imaging (HARDI) is an emerging magnetic resonance imaging (MRI) technique that overcomes some decisive limitations of its predecessor diffusion tensor imaging (DTI). HARDI can resolve locally more than one direction in the diffusion pattern of water molecules and thereby opens up the opportunity to display and track crossing fibers. Showing the local structure of the reconstructed, angular probability profiles in a fast, detailed, and interactive way can improve the quality of the research in this area and help to move it into clinical application. In this paper we present a novel approach for HARDI glyph visualization or, more generally, for the visualization of any function that resides on a sphere and that can be expressed by a Laplace series. Our GPU-accelerated glyph rendering improves the performance of the traditional way of HARDI glyph visualization as well as the visual quality of the reconstructed data, thus offering interactive HARDI data exploration of the local structure of the white brain matter in-vivo. In this paper we exploit the capabilities of modern GPUs to overcome the large, processor-intensive and memory-consuming data visualization.

关键词： data visualization Diffusion tensor imaging Magnetic resonance imaging Image reconstruction Computer graphics Image resolution High-resolution imaging Tensile stress Probability density function Optical fiber testing

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A framework for efficient and scalable execution of domain-specific templates on GPUs

A framework for efficient and scalable execution of domain-s...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Narayanan Sundaram Anand Raghunathan Srimat T. Chakradhar Department of EECS University of California at Berkeley CA USA NEC Laboratories America Princeton NJ USA School of ECE Purdue University IN USA NEC Laboratories America Princeton Princeton NJ USA

graphics processing units (GPUs) have emerged as important players in the transition of the computing industry from sequential to multi- and many-core computing. We propose a software framework for execution of domain-specific parallel templates on GPUs, which simultaneously raises the abstraction level of GPU programming and ensures efficient execution with forward scalability to large data sizes and new GPU platforms. To achieve scalable and efficient GPU execution, our framework focuses on two critical problems that have been largely ignored in previous efforts-processing large data sets that do not fit within the GPU memory, and minimizing data transfers between the host and GPU. Our framework takes domain-specific parallel programming templates that are expressed as parallel operator graphs, and performs operator splitting, of-fload unit identification, and scheduling of off-loaded computations and data transfers between the host and the GPU, to generate a highly optimized execution plan. Finally, a code generator produces a hybrid CPU/GPU program in accordance with the derived execution plan, that uses lower-level frameworks such as CUDA. We have applied the proposed framework to templates from the recognition domain, specifically edge detection kernels and convolutional neural networks that are commonly used in image and video analysis. We present results on two different GPU platforms from NVIDIA (a Tesla C870 GPU computing card and a GeForce 8800 graphics card) that demonstrate 1.7-7.8X performance improvements over already accelerated baseline GPU implementations. We also demonstrate scalability to input data sets and application memory footprints of 6 GB and 17 GB, respectively, on GPU platforms with only 768 MB and 1.5 GB of memory.

关键词： graphics parallel programming Scalability Computer industry Job shop scheduling Processor scheduling Concurrent computing High performance computing Hybrid power systems Central Processing Unit

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Time and Streak Surfaces for Flow visualization in large Time-Varying data Sets

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2009年第6期15卷 1267-1274页

作者： Krishnan, Hari Garth, Christoph Joy, Kenneth I. Univ Calif Davis Inst Data Anal & Visualizat Davis CA 95616 USA

Time and streak surfaces are ideal tools to illustrate time-varying vector fields since they directly appeal to the intuition about coherently moving particles. However, efficient generation of high-quality time and streak surfaces for complex, large and time-varying vector field data has been elusive due to the computational effort involved. In this work, we propose a novel algorithm for computing such surfaces. Our approach is based on a decoupling of surface advection and surface adaptation and yields improved efficiency over other surface tracking methods, and allows us to leverage inherent parallelization opportunities in the surface advection, resulting in more rapid parallel computation. Moreover, we obtain as a result of our algorithm the entire evolution of a time or streak surface in a compact representation, allowing for interactive, high-quality rendering, visualization and exploration of the evolving surface. Finally, we discuss a number of ways to improve surface depiction through advanced rendering and texturing, while preserving interactivity, and provide a number of examples for real-world datasets and analyze the behavior of our algorithm on them.

关键词： 3D vector field visualization flow visualization time-varying time and streak surfaces surface extraction

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A GPU-based architecture for improved online rebinning performance in clinical 3-D PET

A GPU-based architecture for improved online rebinning perfo...

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ieee symposium on Nuclear Science (NSS/MIC)

作者： Dilip R. Patlolla Eric Breeding William F. Jones Jimmy Everman Siemens Healthcare Molecular Imaging Siemens Medical Solutions Inc. Knoxville TN USA

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 large data 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 data parallel 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.

关键词： Positron emission tomography Hardware Telephony Computer architecture Detectors Sensor arrays Biomedical imaging USA Councils graphics parallel processing

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