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检索条件"任意字段=Conference on Parallel Processing for Imaging Applications"

共 765 条记录，以下是181-190 订阅

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Porting Matlab applications to high-performance C++ codes: CPU/GPU-accelerated spherical deconvolution of diffusion MRI data 16th

Porting Matlab applications to high-performance C++ codes: C...

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16th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2016

作者： Blas, Javier Garcia Dolz, Manuel F. Daniel Garcia, J. Carretero, Jesus Daducci, Alessandro Aleman, Yasser Canales-Rodriguez, Erick Jorge Dpto. de Informática Universidad Carlos III de Madrid Leganés Spain Signal Processing Lab École Polytechnique Fédérale de Lausanne Lausanne Switzerland Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain Oviedo Spain FIDMAG Germanes Hospitalàries Barcelona Spain

ISBN: (纸本)9783319495828

In many scientific research fields, Matlab has been established as de facto tool for application design. This approach offers multiple advantages such as rapid deployment prototyping and the use of high performance linear algebra, among others. However, the applications developed are highly dependent of the Matlab runtime, limiting the deployment in heterogeneous platforms. In this paper we present the migration of a Matlab-implemented application to the C++ programming language, allowing the parallelization in GPUs. In particular, we have chosen RUMBA-SD, a spherical deconvolution algorithm, which estimates the intravoxel white-matter fiber orientations from diffusion MRI data. We describe the methodology used along with the tools and libraries leveraged during the translation task of such application. To demonstrate the benefits of the migration process, we perform a series of experiments using different high performance computing heterogeneous platforms and linear algebra libraries. This work aims to be a guide for future developments that are implemented out of Matlab. The results show that the C++ version attains, on average, a speedup of 8× over the Matlab one. © Springer International Publishing AG 2016.

关键词： Magnetic resonance imaging

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parallel approaches to integration with applications in optical coherence tomography

Parallel approaches to integration with applications in opti...

引用

International conference on Signal processing and Communication Systems, ICSPCS

作者： Konstantin Kapinchev Frederick Barnes Sylvain Rivet Adrian Bradu Adrian Podoleanu School of Computing University of Kent Canterbury UK School of Physical Sciences University of Kent Canterbury UK

ISBN: (纸本)9781509009428

This paper presents a comparison between three GPU-based parallel approaches to integration in digital signal processing (DSP). In many cases in practice, the integration is applied on large signals. Its implementation in real-time systems imposes strict limitations on the computational time. In cases where sequential implementation does not meet these limitations, parallel optimization is expected to provide a solution. The discussed parallel approaches are based on the many-core architecture of the GPU. These approaches are employed in generating synthesized scanning laser ophthalmoscopy (SLO) images. These images are based on multiple en-face images, generated by optical coherence tomography (OCT) system. Optimal parallel approaches, which meet the real-time criterion, are identified.

关键词： Graphics processing units Kernel imaging Mathematical model Optimization Image resolution Real-time systems

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Real time ultrasound image denoising using NVIDIA CUDA

Real time ultrasound image denoising using NVIDIA CUDA

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International conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Amira Hadj Fredj Jihene Malek Faculty of Sciences Monastir University Monastir Tunisia

ISBN: (纸本)9781467385275

Image filtering is a process of reducing noise which degrades the performance of image processing. In some applications such as segmentation or classification, denoising has been designed to smooth the homogeneous areas while keeping and enhancing the edges. In several applications such as video analysis, image-guided surgical interventions or visual servoing, real-time denoising is needed. The devoted filter was suggested to adjust the filter of anisotropic diffusion to the features of the multiplicative noise existing within the ultrasound video and also to make easier the filtering process. The purpose of this study is to decrease the processing time implementation of the diffusion function using parallel processors through the optimization of the Graphics processor unit (GPU). The results show that the suggested method is very effictive in terms of real-time compared to a standard central processing unit (CPU) implementation. Our proposed model magnifies the acceleration of the image filtering to 7X compared to the sequential calculation.

关键词： Graphics processing units Ultrasonic imaging Streaming media Real-time systems Speckle Image edge detection Kernel

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Local principal component analysis overcomplete method: A GPU parallel implementation combining shared and global memories

Local principal component analysis overcomplete method: A GP...

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International conference on High Performance Computing & Simulation (HPCS)

作者： Salvatore Cuomo Pasquale De Michele Ardelio Galletti Livia Marcellino Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Dept. of Science and Technology University of Naples Parthenope Naples Italy

Denoising algorithms are widely studied to improve image quality in many applicative fields, such as for example the Magnetic Resonance imaging (MRI). In real scenarios, computationally expensive schemes have been rendered practicable by means of programming methodologies that resort to massively parallel architectures, known as Graphic Processor Units (GPUs). In this paper, we propose an hybrid CPU and GPU parallel implementation of the Overcomplete Local Principal Component Analysis (OLPCA) for the image denoising. We have implemented some computational tasks of the denoising procedure focusing on a strategy that combines both shared and global memories mapping approach on the GPU with the aim to optimize the performance of OLPCA. The experimental results show improvements in terms of GFlops and memory throughput with a promising speedup with respect to the CPU version that encourage its usability in the expensive application of the Diffusion Weighted imaging (DWI) as filter of noisy images.

关键词： Kernel Instruction sets Graphics processing units Principal component analysis Noise measurement Covariance matrices Magnetic resonance imaging

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Towards a smart selection of resources for low-energy multimedia processing

Towards a smart selection of resources for low-energy multim...

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International conference on Cloud Technologies and applications (CloudTech)

作者： Sidi Ahmed Mahmoudi Faculty of Engineering University of Mons Belgium

ISBN: (纸本)9781467388955

Today, one can find images and videos everywhere, they can come from cameras, mobile phones or from other devices. These images and videos are used to illustrate different objects in a large number of situations (airports, hospitals, public areas, sport events, etc.). This makes the task of processing images and videos a necessary tool that can be used for various domains related to computer vision. The performance of these algorithms have been so reduced due to their high intensive computation and energy consumption. In this work, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in case of processing single image, multiple images, multiple videos or single video in real time. This framework enables to affect the CPU or/and GPU units for calculation depending on the type of media to process and the algorithm complexity. The framework provides several image and video functions on GPU, such as silhouette extraction, points of interest extraction, edges detection, sparse and dense optical flow estimation. These functions are exploited in different applications such as vertebra segmentation in X-ray and MR images, gaze estimation, event detection and localization in real time. Experimental results have been obtained by applying the framework for different use case applications showing a speedup ranging from 5 to 116×, by comparison with sequential CPU implementations. In addition to these performance, the parallel and heterogeneous implementation offered lower power consumption as a result for the fast treatment.

关键词： Biomedical imaging Performance evaluation Estimation Cameras Image edge detection Hardware Graphics processing units

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parallelization of reverse time migration using MPI + OpenMP

Parallelization of reverse time migration using MPI + OpenMP

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International conference on Advanced Communication, Control and Computing Technologies (ICACCCT)

作者： S. Kansara Akanksha Gardas Naresh Kumar Gujarat Technological University Gujarat India Center of Development for Advanced Computing Pune India

ISBN: (纸本)9781467395465

Increasing computational power ensures effective implementation of high-end HPC applications but does not guarantee performance. Seismic modelling is one such high computational demanding application. In oil and gas industries, reverse time migration is one of the important migration algorithms that guarantees accurate imaging results but its practical implementation gets hindered due to its high computational cost. In this paper we will discuss reverse time migration for solving 2D wave equations using finite difference method and its parallel implementation using Message Passing Interface(MPI) and OpenMP.

关键词： Computational modeling Propagation Mathematical model Multicore processing imaging conferences Finite difference methods

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Severe burns assessment by joint color-thermal imagery and ensemble methods

Severe burns assessment by joint color-thermal imagery and e...

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International conference on e-health Networking, applications and Services (HealthCom)

作者： Mihai-Sorin Badea Constantin Vertan Corneliu Florea Laura Florea Silviu Bădoiu The Image Processing and Analysis Lab Politehnica University of Bucharest Bucharest Romania “Carol Davila” University of Medicine and Pharmacy Bucharest Romania

Burns are some of the most severe forms of accidental trauma across the world. Burn injuries require specialized care and an early and accurate distinction between superficial dermal burns and deep dermal burns which require further surgical procedures, as they do not heal spontaneously. This paper proposes a multispectral imaging based diagnosis support system for the identification of severe burns. The acquisition is performed in both visual and infrared domains, simultaneously, with a thermal camera recording in parallel color and thermal images of the unconstrained patient and patient environment. The classification of burns is developed under a supervised scenario, according to a ground truth defined by specialist surgeons from a large pediatric case database, by an ensemble of methods that gather votes from both convolutional neural network and standard pattern recognition systems.

关键词： Wounds Skin Databases Feature extraction Image color analysis Surgery Color

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BAYESIAN SPARSE REGULARIZED RECONSTRUCTION IN parallel MRI WITH SENSITIVITY MATRIX IMPRECISION

BAYESIAN SPARSE REGULARIZED RECONSTRUCTION IN PARALLEL MRI W...

引用

International conference on Advances in Biomedical Engineering (ICABME)

作者： Chaari, Lotfi Univ Toulouse IRIT INP ENSEEIHT Toulouse France MIRACL Lab Sfax Tunisia

ISBN: (纸本)9781467365161

parallel MRI is a fast imaging technique that allows reconstruction of full Field-of-View (FoV) images based on under-sampled k-space data acquired using multiple receiver coils with complementary sensitivity profiles. It enables the acquisition of highly resolved images either in space or time, which is of particular interest in applications like functional neuroimaging. These features are counterbalanced by a degraded SNR and the presence of artifacts that depend on the reconstruction algorithm. These artifacts are mainly caused by acquisition noise and imprecisions in the sensitivity matrices, which are a priori estimated. In this paper, we present a novel method for parallel MRI Bayesian regularized reconstruction while accounting for sensitivity maps imperfections and correcting them. The proposed method is validated on realistic simulated data and results show the outperformance of our method even compared to regularized techniques.

关键词： Bayes methods biomedical MRI image reconstruction matrix algebra medical image processing parallel processing Bayesian sparse regularized reconstruction coil sensitivity profiles degraded SNR fast imaging technique full FoV image reconstruction functional neuroimaging highly spatially resolved images highly temporally resolved images imaging artifacts multiple receiver coils parallel MRI Bayesian regularized reconstruction reconstruction algorithm sensitivity map imperfections sensitivity matrix imprecision undersampled k-space data Coils Estimation Image reconstruction Magnetic resonance imaging Sensitivity Sparse matrices Bayes methods image reconstruction Reconstruction algorithm biomedical MRI Functional Neuroimaging Sparse matrix Medical image processing Magnetic Resonance imaging Coils matrix algebra parallel processing (COMPUTERS) Multiple Receiver imaging

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Electrical and Optical TSVs for High Frequency Photonic applications

Electrical and Optical TSVs for High Frequency Photonic Appl...

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Electronic Components and Technology conference (ECTC)

作者： J. Kraft G. Meinhardt K. Molnar T. Bodner F. Schrank Technology Research and Development ams AG Unterpremstaetten Austria

Electronics traditionally takes place in horizontal layers on the surface of a wafer. This is also valid for integrated photonics, where the wave guides are built from layers that are parallel to the wafer surface. The transfer of signals in-between such entities is critical in respect to power loss. This is especially valid for the coupling of light, e.g. the coupling from fibers into or out of the integrated wave guide layers, coupling of light in-between boards and the two sides of a wafer. But also for HF electrical signals the transfer from one side of the wafer to the other side of the wafer by TSVs can be challenging because of high parasitics like the capacitance between the TSV metallization and the wafer substrate. This paper presents a new approach for the parallel processing of TSVs enabling optical TSVs and electrical TSVs capable of data rates up to 40 Gbit/s.

关键词： Through-silicon vias Optical films Resists Optical device fabrication Metallization Optical imaging

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Acceleration of Ultrasonic Data Compression Using OpenCL on GPU

Acceleration of Ultrasonic Data Compression Using OpenCL on ...

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IEEE INTERNATIONAL conference ON ELECTRO/INFORMATION TECHNOLOGY (EIT)

作者： Wang, Boyang Govindan, Pramod Gonnot, Thomas Saniie, Jafar IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9781479988020

Ultrasonic imaging applications require real-time acquisition and processing of huge volume of data. Subsequently, reducing the data storage becomes an essential requirement for applications involving ultrasound technology. Compression of the acquired data significantly reduces the storage and also helps in faster data processing. Furthermore, computationally efficient data compression enables real-time data transfer to remote locations for expert analysis. In this study, an efficient implementation of ultrasonic data compression based on discrete wavelet transform (DWT) is realized on GeForce GT 750M NVIDIA graphical processing unit (GPU) using Open Computing Language (OpenCL). Execution of OpenCL program on GPU allows parallel computing of the compression algorithm to accelerate the computational performance. This study demonstrates that OpenCL implementation of ultrasonic compression algorithm on GPU requires only 0.29 seconds for compressing 33 Mbytes of ultrasonic data into 6.6 Mbytes (80% compression), which makes it suitable for real-time ultrasonic imaging applications.

关键词： Data compression Compression algorithms GRAPPER PICK UP Graphics processing Unit acceleration Ultrasonography Ultrasonics Data Storage Remote locations real-time data parallel processing (COMPUTERS)

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