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Performance Evaluation of Medical image Similarity Analysis in a Heterogeneous Architecture

Performance Evaluation of Medical Image Similarity Analysis ...

引用

Annual IEEE symposium on computer-Based Medical Systems

作者： José Raniery Ferreira Marcelo Costa Oliveira André Lage Freitas Universidade Federal de Alagoas Maceio Alagoas BR Lab of Telemedicine and Medical Informatics at University Hospital (HUPPA) Federal University of Alagoas Maceio Alagoas Brazil

The volume of data has increased fast, particularly medical images, in big hospitals over the last years. This increase imposes a big challenge to medical specialists: the maintenance of high interpretation accuracy of image-based diagnosis. computer-Aided Diagnosis software allied to the Content-Based image Retrieval (CBIR) can provide decision support to specialists by allowing them to find images from a database that are similar to a reference image. However, a well known challenge of CBIR is the processing time that it takes to process all comparisons between the reference image and the image database. This paper proposes a performance evaluation of medical image Similarity Analysis (ISA) in a heterogeneous single-, multi- and many-core architecture using the high performance parallel OpenCL framework. A CBIR algorithm was implemented to validate the proposal. The algorithm used a Lung Cancer image database with 131, 072 Computed Tomography scans, Texture Attributes for image features and Euclidean Distance for image comparison metrics. The results showed that the OpenCL parallelism can increase the performance of ISA, especially using the GPU, with speedups of 3x, 36x and 64x. The results also showed that it is not worth the use of GPU local memory for the Euclidean Distance metrics due to its low performance improvement and high implementation complexity in comparison to the GPU global memory. That being said, GPU is a safer medical CBIR approach than further distributed environments as clusters, cloud and grid computing because GPU usage does not require the patient data to be transfered to other machines.

关键词： graphics processing units Medical diagnostic imaging computer architecture Algorithm design and analysis Kernel Performance evaluation

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A GPU implementation of the inverse fast multipole method for multi-bistatic imaging applications

A GPU implementation of the inverse fast multipole method fo...

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Antennas and Propagation Society International symposium

作者： Luis E. Tirado Galia Ghazi José Á. Martínez-Lorenzo Carey M. Rappaport Yuri Álvarez Fernando Las-Heras Department of Electrical Engineering Northeastern University Boston MA USA Área de Teoría de la Señal y Comunicaciones Universidad de Oviedo Gijón Asturias Spain

This paper presents a parallel implementation of the Inverse Fast Multipole Method (IFMM) for multi-bistatic imaging configurations. NVIDIA's Compute Unified Device Architecture (CUDA) is used to parallelize and accelerate the imaging algorithm in a graphics processing Unit (GPU). The algorithm is validated with experimental data, collected by a Frequency-Modulated Continuous Wave (FMCW) radar system operating in the 70-77 GHz frequency band. The proposed GPU-based IFMM algorithm accelerated the single-core CPU version by a factor of 46.

关键词： graphics processing units image reconstruction Receivers Radar imaging Imaging computer architecture Algorithm design and analysis

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Increasing efficiency of the sequential algorithms programs execution using CUDA

Increasing efficiency of the sequential algorithms programs ...

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Liberios Vokorokos Miroslav Hartinger Norbert Ádám Eva Chovancová Ján Radušovský Department of Computer and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovak Republic

The ambition to achieve higher computing performance, is based on using all the advantages of parallel approach. The devices, software development kits and other technologies are leaping forward. Nowadays, they allow programmer to use more effective and optimized template libraries. They empower him to acces functions of graphics processing unit through the highlevel language. This paper details the experience of using such optimization on basic and more complex algorithms and the measurement of overall effectiveness increase. Our experimental results demonstrate the overall acceleration on various types of GPU.

关键词： graphics processing units image color analysis Libraries Algorithm design and analysis Optimization Performance evaluation Random access memory

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A MULTILEVEL PARALLEL AND SCALABLE SINGLE-HOST GPU CLUSTER FRAMEWORK FOR LARGE-SCALE GEOSPATIAL DATA processing

A MULTILEVEL PARALLEL AND SCALABLE SINGLE-HOST GPU CLUSTER F...

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IEEE International Geoscience and Remote Sensing symposium

作者： Grant J. Scott Kirk Backus Derek T. Anderson University of Missouri Center for Geospatial Intelligence Columbia Missouri USA Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS USA

Geospatial data exists in a variety of formats, including rasters, vector data, and large-scale geospatial databases. There exists an ever-growing number of sensors that are collecting this data, resulting in the explosive growth and scale of high-resolution remote sensing geospatial data collections. A particularly challenging domain of geospatial data processing involves mining information from high resolution remote sensing imagery. The prevalence of high-resolution raster geospatial data collections represents a significant data challenge, as a single remote sensing image is composed of hundreds of millions of pixels. We have developed a robust application framework which exploits graphics processing unit (GPU) clusters to perform high-throughput geospatial data processing. We process geospatial raster data concurrently across tiles of large geospatial data rasters, utilizing GPU co-processors driven by CPU threads to extract refined geospatial information. The framework can produce output rasters or perform image information mining to write data into a geospatial database.

关键词： graphics processing units Geospatial analysis Data processing Instruction sets Remote sensing Parallel processing Data mining

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Real-time virtual view rendering for video communication systems using OpenCL

Real-time virtual view rendering for video communication sys...

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IEEE International symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Tobias Lange Goran Petrovic Thorsten Herfet Telecommunications Lab Saarland University

Multi-view video (MVV) offers many possibilities for video communication systems. The creation of virtual views allows to change the viewer's perspective interactively on the receiver side. In this paper we propose a real time algorithm capable of rendering virtual views with a high image quality without the support of scene geometry information. We utilize the highly parallel architecture of recent GPUs for online depth estimation and a sophisticated but generally available shader architecture for efficient rendering. Using these methods we are able to render virtual views with resolutions of up to 3600×3000 with a frame rate of more than 25 frames per second.

关键词： Rendering (computer graphics) Cameras Real-time systems Estimation Streaming media Geometry graphics processing units

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Evaluation of a Feature Tracking Vision Application on a Heterogeneous Chip

Evaluation of a Feature Tracking Vision Application on a Het...

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International symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Rubén Gran August Shi Ehsan Totoni María J. Garzarán University of Zaragoza Zaragoza Spain University of Illinois at Urbana-Champaign Urbana IL USA

Consumers of personal devices such as desktops, tablets, or smart phones run applications based on image or video processing, as they enable a natural computer-user interaction. The challenge with these computationally demanding applications is to execute them efficiently. One way to address this problem is to use on-chip heterogeneous systems, where tasks can execute in the device where they run more efficiently. In this paper, we discuss the optimization of a feature tracking application, written in OpenCL, when running on an on-chip heterogeneous platform. Our results show that OpenCL can facilitate programming of these heterogeneous systems because it provides a unified programming paradigm and at the same time can deliver significant performance improvements. We show that, after optimization, our feature tracking application runs 3.2, 2.6, and 4.3 times faster and consumes 2.2, 3.1, and 2.7 times less energy when running on the multicore, the GPU, or both the CPU and the GPU of an Intel i7, respectively.

关键词： graphics processing units Vectors Multicore processing Optimization Performance evaluation Streaming media Hardware

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Multi-phase Identification in Microstructures images Using a GPU Accelerated Fuzzy C-Means Segmentation

Multi-phase Identification in Microstructures Images Using a...

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International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Darian M. Onchis Doina Frunzaverde Mihail Gaianu Relu Ciubotariu Department of Mechanics and Materials Engineering Eftimie Murgu University Resita Romania Department of Computer Science West University of Timisoara Timisoara Romania

This paper presents an effective algorithm for the identification of multiple phases in microstructures images. The procedure is based on an efficient image segmentation using the fuzzy c-means algorithm. Furthermore, the algorithm is accelerated on a GPU cluster in order to obtain optimal computing times for large size images. The results are compared on the same experimental images with the ones obtained from a commercial software and the accuracy of the proposed algorithm is demonstrated.

关键词： graphics processing units image segmentation Clustering algorithms Algorithm design and analysis Acceleration Software algorithms

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Liver Segmentation in Contrast Enhanced MR Datasets Using a Probabilistic Active Shape and Appearance Model

Liver Segmentation in Contrast Enhanced MR Datasets Using a ...

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Annual IEEE symposium on computer-Based Medical Systems

作者： Klaus Drechsler Anton Knaub Cristina Oyarzun Laura Stefan Wesarg Cognitive Computing & Medical Imaging Fraunhofer Institute for Computer Graphics Research Darmstadt Germany

The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets.

关键词： Shape Liver Standards image segmentation Active shape model Probabilistic logic Computed tomography

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Real-time 3D data reduction and reproduction of spatial model using line detection in RGB image

Real-time 3D data reduction and reproduction of spatial mode...

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International symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Tomoko Yonezawa Ken Ueda Kansai University Takatsuki Osaka Japan Nova system Co. Ltd

We propose a real-time polygon reduction method for online reproduction of a three-dimensional spatial model using image processing. Currently, Microsoft Kinect is a popular device for capturing a wide-area and detailed depth image. When producing 3D data reconstruction from the depth image and the RGB image as well as when transmitting 3D data in realtime, it is important to reduce the data size. In this paper, we introduce a polygon reduction method that uses line detection for architectural surfaces and their joint lines in an RGB image. The system discards most of the depth information, leaving the representative value of the surface with part of the RGB image. The remaining data are used to reconstruct simple polygon data drawn by point-cloud or texture mappings. From the operation test of our proposed method, it is confirmed that the polygon reduction could reduce data without increasing the time duration of 3D reconstruction.

关键词： Three-dimensional displays image reconstruction Real-time systems Surface reconstruction Cameras Rendering (computer graphics) image segmentation

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FPGA Implementation of EM Algorithm for 3D CT Reconstruction

FPGA Implementation of EM Algorithm for 3D CT Reconstruction

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Annual IEEE symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Young Kyu Choi Jason Cong Di Wu Computer Science Department University of California Los Angeles CA USA

Although the expectation maximization (EM)based 3D computed tomography (CT) reconstruction algorithm lowers radiation exposure, its long execution time hinders practical usage. To accelerate this process, we introduce a novel external memory bandwidth reduction strategy by reusing both the sinogram and the voxel intensity. Also, a customized computing engine based on field-programmable gate array (FPGA) is presented to increase the effective memory bandwidth. Experiments on actual patient data show that 85X speedup can be achieved over single-threaded CPU.

关键词： Computed tomography Field programmable gate arrays Bandwidth graphics processing units image reconstruction computer architecture Random access memory

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