imaging data within the clinical practice in general uses standardized formats such as Digital imaging and Communications in Medicine (DICOM). Aside from 3D volume data, DICOM files usually include relational and sema...
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ISBN:
(纸本)9781510640320
imaging data within the clinical practice in general uses standardized formats such as Digital imaging and Communications in Medicine (DICOM). Aside from 3D volume data, DICOM files usually include relational and semantic description information. The majority of current applications for browsing and viewing DICOM files online handle the image volume data only, ignoring the relational component of the data. Alternatively, implementations that show the relational information are provided as complete pre-packaged solutions that are difficult to integrate in existing projects and workflows. This publication proposes a modular, client-side web application for viewing DICOM volume data and displaying DICOM description fields containing relational and semantic information. Furthermore, it supports conversion from DICOM data sets into the nearly raw raster data (NRRD) format, which is commonly utilized for research and academic environments, because of its simpler, easily processable structure, and the removal of all patient DICOM tags (anonymization). The application was developed in JavaScript and integrated into the online medical imageprocessing framework StudierFenster (http://***/). Since our application only requires a standard web browser, it can be used by everyone and can be easily deployed in any wider project without a complex software architecture.
The compressive sensing (CS) technique is a novel tool used to reconstruct images using fewer samples, normally sparse in the transform domain, than those required by conventional imagingsystems. However, the methods...
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ISBN:
(纸本)9781510635692
The compressive sensing (CS) technique is a novel tool used to reconstruct images using fewer samples, normally sparse in the transform domain, than those required by conventional imagingsystems. However, the methods applied for signal reconstruction within the CS approach still present some problems in the implementation, mainly due to their intensive computational demand and high power consumption requirements. These drawbacks need addressing if this approach is followed in systems aimed at e.g. drone autonomous flying or other embedded applications that additionally require very short processing times. In this paper we evaluate the use of hardware based parallelprocessing architecture for the implementation of the Orthogonal Matching Pursuit (OMP) algorithm, one of the most efficient CS reconstruction algorithms developed so far. To improve the algorithm performance, we target different maximum allowed processing times to reach minimum image resolutions required by each system of interest using different sparse (16 and 64) amounts of single-pixel generated samples per image. We also target the final image resolution to be above 20 dB in terms of the peak signal-to-noise ratio (PSNR). To reduce the execution and processing times required to generate each image, we propose implementing parallel kernels in the hardware platform for each of the operations required by the algorithms under study. In the proposed implementation the reconstructed images are used to generate video streams that form the foundation on which decisions are to be made by the system in continuous time, whereby each single image (frame) reconstruction cannot overcome 30 ms in order to maintain the minimum amount of frames per second (fps) above 33 (minimum required for an acceptable video stream). The implementation of a variation of the OMP algorithm in a graphics processing unit (GPU) using parallel architecture approach allows obtaining processing times 4 or 5 times shorter than those obtai
Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in a...
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Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in accuracy. processing in memory (PIM) computes data in-place while having high memory density and supporting bit-parallel operations with low energy consumption. In this article, we propose COSMO, an architecture for computing with stochastic numbers in memory, which enables SC in memory. The proposed architecture is general and can be used for a wide range of applications. It is a highly dense and parallel architecture that supports most SC encodings and operations in memory. It maximizes the performance and energy efficiency of SC by introducing several innovations: (i) in-memory parallel stochastic number generation, (ii) efficient implication-based logic in memory. (iii) novel memory bit line segmenting. (iv) a new memory-compatible SC addition operation, and (v) enabling flexible block allocation. To show the generality and efficiency of our stochastic architecture, we implement imageprocessing. deep neural networks (DNNs), and hyperdimensional (HD) computing on the proposed hardware. Our evaluations show that running DNN inference on COSMO is 141x faster and 80 x more energy efficient as compared to GPU.
image defogging has important application value in preprocessing technology and computer vision system. Dark channel prior (DCP) is simple and effective in many defogging algorithms, but the time-consuming sorting com...
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image defogging has important application value in preprocessing technology and computer vision system. Dark channel prior (DCP) is simple and effective in many defogging algorithms, but the time-consuming sorting comparison and a large number of calculation refinement processes limit its real-time processing capabilities. For real-time applications, we proposed a hardware architecture for single-image defogging, which gives full play to hardware parallelprocessing capability and algorithmic parallelism. First, an average statistical approach is used to estimate atmospheric light. Then, the refined dark channel map is used for transmittance estimation to reduce the blocking effect. The transmittance is linearly corrected to prevent color distortion in outdoor scenes containing sky areas. Finally, a guided filter algorithm is introduced in the transmittance refinement, and its fast mean filter uses an adaptive window to process the image boundary. The hardware implementation of the proposed method uses field programmable gate array device is Zynq-7000. Experimental results show that our design obtains good performance with low-complex hardware implementation and shorter execution time. It only takes 7.43 ms to process a 1280 x 720 image, and the frame rate can reach 135 fps at a clock rate of 125 MHz, which can be used as a real-time hardware accelerator for imageprocessing. (C) 2022 SPIE and IS&T
Deep Neural Networks (DNNs) have emerged as an important class of machine learning algorithms, providing accurate solutions to a broad range of applications. Sparsity in activation maps in DNN training presents an opp...
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Deep Neural Networks (DNNs) have emerged as an important class of machine learning algorithms, providing accurate solutions to a broad range of applications. Sparsity in activation maps in DNN training presents an opportunity to reduce computations. However, exploiting activation sparsity presents two major challenges: i) profiling activation sparsity during training comes with significant overhead due to computing the degree of sparsity and the data movement;ii) the dynamic nature of activation maps requires dynamic dense-to-sparse conversion during training, leading to significant overhead. In this article, we present Spartan, a lightweight hardware/software framework to accelerate DNN training on a GPU. Spartan provides a cost-effective and programmer-transparent microarchitectural solution to exploit activation sparsity detected during training. Spartan provides an efficient sparsity monitor, a tile-based sparse GEMM algorithm, and a novel compaction engine designed for GPU workloads. Spartan can reduce sparsity profiling overhead by 52.5x on average. For the most compute-intensive layers, i.e., convolutional layers, we can speedup AlexNet by 3.4x, VGGNet-16 by 2.14x, and ResNet-18 by 2.02x, when training on the imageNet dataset.
This paper introduces the capabilities and availability of a customizable scientific software package called Livermore Tomography Tools (LTT) built for computed tomography (CT) research. It was initially developed to ...
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This paper introduces the capabilities and availability of a customizable scientific software package called Livermore Tomography Tools (LTT) built for computed tomography (CT) research. It was initially developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. Our goals were to provide quantitatively accurate results reported in physical units (e.g., mm(-1) or cm(-1)) while exploiting all available computational advantages to maximize speed and conserve memory. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac;laptops to supercomputers). As a result, LTT can: process data acquired from various custom-built and commercially available CT scanners, model and simulate xray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms. We describe LTT's software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT's accuracy, speed, and flexibility relative to other solutions.
Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have b...
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Developments in magnetic resonance imaging (MRI) in the last decades show a trend towards a growing number of array coils and an increasing use of a wide variety of sensors. Associated cabling and safety issues have been addressed by moving data acquisition closer to the coil. However, with the increasing number of radio-frequency (RF) channels and trend towards higher acquisition duty-cycles, the data amount is growing, which poses challenges for throughput and data handling. As it is becoming a limitation, early compression and preprocessing is becoming ever more important. Additionally, sensors deliver diverse data, which require distinct and often low-latency processing for run-time updates of scanner operation. To address these challenges, we propose the transition to reconfigurable hardware with an application tailored assembly of interfaces and real-time processing resources. We present an integrated solution based on a system-on-chip (SoC), which offers sufficient throughput and hardware-based parallelprocessing power for very challenging applications. It is equipped with fiber-optical modules serving as versatile interfaces for modular systems with in-field operation. We demonstrate the utility of the platform on the example of concurrent imaging and field sensing with hardware-based coil compression and trajectory extraction. The preprocessed data are then used in expanded encoding model based image reconstruction of single-shot and segmented spirals as used in time-series and anatomical imaging respectively.
In smart systems context, the storage and distribution of health-critical data - medical images, test reports, clinical information etc. that is processed and transmitted via web portal and pervasive devices which req...
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In smart systems context, the storage and distribution of health-critical data - medical images, test reports, clinical information etc. that is processed and transmitted via web portal and pervasive devices which requires a secure and efficient management of patients' medical records. The reliance on centralized data centers in the cloud to process, store, and transmit patients' medical records poses some critical challenges including but not limited to operational costs, storage space requirements, and importantly threats and vulnerabilities to the security and privacy of health-critical data. To address these issues, this research proposes a framework and provides a proof-of-the-concept named Patient-Centric Medical image Management System (PCMIMS). The proposed solution PCMIMS utilizes the Ethereum blockchain and Inter-Planetary File System (IPFS) to enable secure and decentralized storage capabilities that lack in existing solution for patients' medical image management. The PCMIMS design facilitates secure access to Patient-Centric information for health units, patients, medics, and third-party requestors by incorporating the Patient-Centric access control protocol, ensuring privacy and control over medical data. The proposed framework is validated through the deployment of a prototype based on smart contract executed on Ethereum TESTNET blockchain that demonstrates efficiency and feasibility of the solution. Validation results highlight a correlation between (i) number of transactions (i.e., data storage and retrieval), (ii) gas consumption (i.e., energy efficiency), and (iii) data size (volume of Patient-Centric medical images) via repeated trials in Microsoft Windows environment. Validation results also indicate computational efficiency of the solution in terms of processing three most common types of Patient-Centric medical images namely (a) Magnetic resonance imaging (MRI) (b) x-radiation (x-Rays), (c) Computed tomography (CT) scan. This research primaril
Nowadays, imaging and spectroscopy systems operating in the long-wavelength infrared range (LWIR) are rapidly developed and extensively applied in numerous demanding branches of science and technology. This pushes fur...
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ISBN:
(纸本)9781510637191
Nowadays, imaging and spectroscopy systems operating in the long-wavelength infrared range (LWIR) are rapidly developed and extensively applied in numerous demanding branches of science and technology. This pushes further developments into the realms of improving the sensitivity and performance of the LWIR systems, as well as reducing their dimensions and cost. Among modern LWIR technologies, uncooled shutterless bolometric matrices form a favorable platform for addressing these challenging problems, being technologically reliable, compact, and cost-effective. Nevertheless, such detectors features high noises and require real-time digital signal processing. In this work, consisted of two parts, we developed a portable LWIR camera, which relies on a commercial uncooled bolometric matrix, and proposed few approaches aimed at the image acquisition improvement. The first part describes algorithms for image calibration. These algorithms were implemented experimentally in a processing module relying on the Field-Programmable Gate Array (FPGA) and the high-speed double data rate Synchronous Dynamic Random Access Memory (SDRAM). The developed LWIR camera holds strong potential in such applications, as non-destructive sensing and medical imaging.
The necessity of efficient hardware accelerators for imageprocessing kernels is a well known problem. Unlike the conventional HDL based design process, High-level Synthesis (HLS) can directly convert behavioral (C/C+...
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ISBN:
(数字)9781665474047
ISBN:
(纸本)9781665474054
The necessity of efficient hardware accelerators for imageprocessing kernels is a well known problem. Unlike the conventional HDL based design process, High-level Synthesis (HLS) can directly convert behavioral (C/C++) description into RTL code and can reduce design complexity, design time as well as provide user opportunity for design space exploration. Due to the vast optimization possibilities in HLS, a proper application level behavioral characterization is necessary to understand the leverages offered by these workloads especially for facilitating parallel computation. In this work, we present a set of HLS optimization strategies derived upon exploiting the most general HLS influential characteristic features of imageprocessingalgorithms. We also present an HLS benchmark suite imageSpec to demonstrate our strategies and their efficiency in optimizing workloads spanning diverse domains within imageprocessing sector. We have shown that an average performance to hardware gain of 143x could be achieved over the baseline implementation using our optimization strategies.
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