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Dual-domain cascade of U-nets for multi-channel magnetic resonance image reconstruction

MAGNETIC RESONANCE IMAGING 2020年 71卷 140-153页

作者： Souza, Roberto Bento, Mariana Nogovitsyn, Nikita Chung, Kevin J. Loos, Wallace Lebel, R. Marc Frayne, Richard Univ Calgary Hotchkiss Brain Inst Radiol & Clin Neurosci Calgary AB Canada Foothills Med Ctr Seaman Family MR Res Ctr Calgary AB Canada Foothills Med Ctr Calgary Image Proc & Anal Ctr CIPAC Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Dept Psychiat Calgary AB Canada Univ Calgary Mathison Ctr Mental Hlth Res & Educ Calgary AB Canada Univ Western Ontario Dept Med Biophys London ON Canada Gen Elect Healthcare Calgary AB Canada

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for mull-channel magnetic resonance (MR) image reconstruction. The two-element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net ii, b) W-net KK, c) W-net IK, and d) W-net KI. Selected four element (WW-nets) and six element (WWW-nets) networks were also examined. Two configurations of each network were compared: 1) each coil channel was processed independently, and 2) all channels were processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal-to-noise ratio and visual information fidelity were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain were better when independently processing individual channels of multi-channel data. Dual-domain methods were better when simultaneously reconstructing all channels of mull-channel data. In addition, the best cascade of U-nets performed better (p < 0.01) than the previously published, state-of-the-art Deep Cascade and Hybrid Cascade models in three out of four experiments.

关键词： Magnetic resonance imaging Compressed sensing (CS) parallel imaging (PI) Multi-channel (coil) image reconstruction Inverse problems Brain imaging Machine learning

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一种基于Spark的公路裂缝图像处理方法

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计算机与数字工程 2022年第8期50卷 1795-1801页

作者：李杨刘庆华刘东华黄凯枫贾叙文江苏科技大学镇江212000

随着公路修建里程增长,为了更高效率地解决路面养护问题,对路面裂缝检测技术进行研究。论文将传统的图像分割处理与Spark结合,提出了一种基于Spark的路面裂缝图像分割处理方法。先将图像预处理转化为二进制文件,读入到分布式文件系统;... 详细信息

随着公路修建里程增长,为了更高效率地解决路面养护问题,对路面裂缝检测技术进行研究。论文将传统的图像分割处理与Spark结合,提出了一种基于Spark的路面裂缝图像分割处理方法。先将图像预处理转化为二进制文件,读入到分布式文件系统;然后通过传递函数,对裂缝进行图像分割来获得裂缝区域;最后,以局部自适应阈值算法结合Spark平台来进行图像的并行处理,完成裂缝的高效率提取。以在校园拍摄的路面图像数据为实验数据,分别在处理时间和检测准确率这两方面进行实验。由结果可知:利用Spark对数千张图像数据进行计算时,在确定千余张图像数据规模时,双节点比单节点处理时间更少,最多减少了20.4%。在图像分割的准确率方面,已经由之前方法的47.5%提高到了86.3%。实验结果表明,基于Spark的裂缝图像处理,结合局部自适应阈值分割算法进行数千张裂缝图像分割,能有效提升传统方法的处理效率,能实现大规模图像的并行处理。

关键词：公路裂缝图像处理 Spark 并行处理大数据局部自适应阈值分割

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Zero-touch security automation mechanisms for edge NFV: the π-Edge approach

Zero-touch security automation mechanisms for edge NFV: the ...

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International Conference on Network and Service Management

作者： Alexandros Valantasis Nikos Psaromanolakis Vasileios Theodorou Intracom Telecom Athens Greece

ISBN: (数字)9783903176515

ISBN: (纸本)9781665489928

The shift towards distributed computing architectures that push data storage and processing to the edge of the network, is resulting into a convergence of cloud-computing services and next generation mobile network technologies. In order to uniformly manage resources and services in the formed cloud/core to edge/devices continuum and to handle the diversity of multi-party underlying infrastructure technologies in a latency-aware, reliable and trustworthy fashion, management automation has become more crucial than ever. In this work, we present the security analytics mechanisms of the π-Edge platform–our edge management platform that embodies zero-touch automation features for interoperability, Quality of Service (QoS) assurance, resilience and trust. To this end, we introduce a declarative NFV MANO Information Model (IM) and methods for automatically enhancing Network Slices at the edge, with security services that i) continuously monitor user-plane traffic on the links between Virtual Network Functions (VNFs), ii) detect possible network vulnerabilities or malicious behaviour and iii) apply relevant actions to effectively observe and mitigate identified threats. The implementation of such mechanisms is evaluated through experimentation on a use case of DDoS attacking scenarios, showcasing the usability and the benefits of our proposed solution.

关键词： Cloud computing Automation image edge detection Quality of service Time measurement Security Resource management

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A Raster Data Framework Based on distributed Heterogeneous Cluster

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第4期47卷 715-723页

作者： Bhushan, R. Phani Somayajulu, D. V. L. N. Venkatraman, S. Subramanyam, R. B. V. ADRIN Dept Space Hyderabad Telangana India NIT Dept Comp Sci & Engn Warangal Andhra Pradesh India

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10-12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods.

关键词： Big data Remote sensing Spatial MapReduce HDFS RecordReader Raster data Vector data Resource scheduler Retrieval Engine

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The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

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REMOTE SENSING 2020年第1期12卷 39-39页

作者： Halladin-Dabrowska, Anna Kania, Adam Kopec, Dominik MGGP Aero Sp Zoo PL-33100 Tarnow Poland Defin Sp Zoo PL-52116 Wroclaw Poland Univ Lodz Fac Biol & Environm Dept Geobot & Plant Ecol PL-90237 Lodz Poland

Supervised classification methods, used for many applications, including vegetation mapping require accurate "ground truth" to be effective. Nevertheless, it is common for the quality of this data to be poorly verified prior to it being used for the training and validation of classification models. The fact that noisy or erroneous parts of the reference dataset are not removed is usually explained by the relatively high resistance of some algorithms to errors. The objective of this study was to demonstrate the rationale for cleaning the reference dataset used for the classification of heterogeneous non-forest vegetation, and to present a workflow based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm for the better integration of reference data with remote sensing data in order to improve outcomes. The proposed analysis is a new application of the t-SNE algorithm. The effectiveness of this workflow was tested by classifying three heterogeneous non-forest Natura 2000 habitats: Molinia meadows (Molinion caeruleae;code 6410), species-rich Nardus grassland (code 6230) and dry heaths (code 4030), employing two commonly used algorithms: random forest (RF) and AdaBoost (AB), which, according to the literature, differ in their resistance to errors in reference datasets. Polygons collected in the field (on-ground reference data) in 2016 and 2017, containing no intentional errors, were used as the on-ground reference dataset. The remote sensing data used in the classification were obtained in 2017 during the peak growing season by a HySpex sensor consisting of two imaging spectrometers covering spectral ranges of 0.4-0.9 mu m (VNIR-1800) and 0.9-2.5 mu m (SWIR-384). The on-ground reference dataset was gradually cleaned by verifying candidate polygons selected by visual interpretation of t-SNE plots. Around 40-50% of candidate polygons were ultimately found to contain errors. Altogether, 15% of reference polygons were removed. As a result, the quality of th

关键词： data pre-processing hyperspectral data image classification visualization dimensionality reduction random forest Natura 2000 grassland

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Study of large axial space rebinning algorithm in γ-photon industrial tomography image reconstruction

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JOURNAL OF INSTRUMENTATION 2021年第12期16卷

作者： Wang, M. Zhao, M. Yao, M. Liu, J. Guo, R. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn 29 Gen Rd Nanjing 211106 Peoples R China

The accuracy of the existing single slice and Fourier rebinning algorithms depends on the projection angle of the line of response. The increase of such projection angle with the detector size, typical in the large axial space of gamma-photon industrial detection, and the loss of some projection data after rebinning, result in the degradation of the image quality. In addition, those algorithms consider the probability of positron annihilation equally distributed along the line of response, which prevents to estimate accurately the positions of the annihilation point, and can originate artifacts and noise in the reconstructed image. In this work, we propose an alternative large axial space rebinning algorithm. In that algorithm, initially the line of response is divided into transverse and axial components. Then, each line of response is uniformly rebinned into all the 2D sinogram data intersecting with it. To improve the accuracy of the estimate of the annihilation point location and suppress the noise effectively, we assign a Gaussian weight coefficient to the projection data, and optimise the rebinning algorithm with it. Finally, we reconstruct the image on the basis of the 2D sinograms with the optimised weights. On the computational side, the algorithm is also accelerated by making use of parallel computing. Both simulation and experimental results show that the proposed method improves the contrast and spatial resolution of 2D reconstructed images. Furthermore, the reconstruction time is not affected by the new method, which is therefore expected to meet the demand of gamma-photon industrial inspection imaging.

关键词： Data processing methods Detection of defects Inspection with gamma rays

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Evaluation of suturing performance in general surgery and ocular microsurgery by combining computer vision-based software and distributed fiber optic strain sensors: a proof-of-concept

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第8期15卷 1359-1367页

作者： Handelman, Amir Keshet, Yariv Livny, Eitan Barkan, Refael Nahum, Yoav Tepper, Ronnie Holon Inst Technol Fac Engn Dept Elect Engn Holon Israel Clalit Hlth Serv Simultech Ctr Med Simulat Meir Hosp Med Ctr Kefar Sava Israel Rabin Med Ctr Dept Ophthalmol Petah Tiqwa Israel Tel Aviv Univ Sackler Fac Med Tel Aviv Israel

Purpose Improper suturing may cause an inadequate wound healing process and wound dehiscence as well as infection and even graft rejection in case of corneal transplantation. Hence, training surgeons in correct suturing procedures and objectively assessing their surgical skills is desirable. methods Two complementary methods for assessment of suturing skills in two medical fields (general surgery and ocular microsurgery) were demonstrated. Suturing quality is assessed by computer vision software. Evaluation of stitching flow of operation is based on measuring strain induced in an optical fiber that is placed in proximity to the wound and parallel thereto and is pressed and passed by wound stitches. Results Our software generated a score for suturing outcome in both general surgery and ocular microsurgery when the stitching was done on a patch. Every trainee received a score in the range 0-100 that describes his/her performance. Strain values were recognized when using a patch in general surgery and a rubber patch in ocular microsurgery, but were less distinct in (disqualified) human cornea. Conclusions We proved a concept of an objective scoring method (based on various image processing algorithms) for assessment of suturing performance. It was also shown that fiber optic strain sensors are sensitive to the flow of stitching operation on a patch but are less sensitive to the flow of stitching operation on a human cornea. By combining these two methods, we can comprehensively evaluate the suturing performance objectively.

关键词： Sutures Fiber optic sensors Computer vision Ocular microsurgery Surgical education

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Underwater image Enhancement based on Dehazing and Color Correction 17

Underwater Image Enhancement based on Dehazing and Color Cor...

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IEEE Int Conf on parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Li, Hanyu Zhuang, Peixian Wang, Wei Li, Jingjing Nanjing Univ Informat Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728143286

Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requirements concerning the underwater research due to the serious image degradation. To enhance single underwater image, we propose an effective and new method combined on the dehazing and color correction algorithm. First, we obtain the dehazed image by a fusion dehazing method calculated on the difference between maximum green-blue dark channels and maximum red dark channel, and the top 0.1% bright pixels in green-blue dark channels. Second, we obtain the enhanced image via the color restoration method corresponding to the human visual system. Finally, we use an efficient and simple weight fusion strategy to incorporate the dehazed image and the enhanced image for yielding the final high quality underwater image. Subjective assessment demonstrates that our method can remove haze, correct color cast, improve image brightness, and preserve image naturalness. The enhanced results obtain the highest values in terms of entropy, average gradient, UCIQE, UIQM, and CCF, which is superior to several existing underwater enhancement methods. Moreover, our method can improve other degraded image quality, such as low-light image and haze image, and can benefit the application tests, such as key points matching and edge detection.

关键词： Single underwater image enhancement image dehazing retinex color correction

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A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images

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SIGNAL processing 2021年 178卷 107793-107793页

作者： Li, Bo Peng, Hong Wang, Jun Xihua Univ Sch Comp & Software Engn Chengdu 610039 Sichuan Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China

Dynamic threshold neural P systems (DTNP systems) are a distributed parallel computing model with an interesting mechanism involving the cooperative spiking of neurons in a local region. In this paper, this mechanism is combined with the nonsubsampled contourlet transform (NSCT) to develop a novel fusion method for multi-modality medical images. The complementary information of multi-modality images is extracted using an improved novel sum-modified Laplacian (INSML) feature, which is used in the fusion rules for the low-frequency NSCT coefficients. Moreover, the high-frequency NSCT coefficients are extracted using the WLE-INSML features, which are used to construct the fusion rules for these coefficients. The proposed fusion method is evaluated on an open dataset consisting of twelve pairs of multi-modality medical images. In addition, it is compared with nine previously reported fusion methods and four deep learning based fusion methods. The qualitative and quantitative experimental results demonstrate the advantage of the proposed fusion method in terms of the visual quality and fusion performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Dynamic threshold neural P systems Nonsubsampled contourlet transform image fusion Multi-modality medical images

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parallel coordinates visualization in the ELK stack 30

Parallel coordinates visualization in the ELK stack

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30th International Conference on Computer Graphics and Machine Vision, GraphiCon 2020

作者： Galkin, Timofei Grigorieva, Maria National Research Nuclear University "MEPhI" Kashirskoe shosse 31 Moscow115409 Russia Scientific Research Computing Center Lomonosov Moscow State University Leninskie Gory 1 p.4 Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Leninskie Gory 1 Moscow119991 Russia

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in resources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the ElasticSearch storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heatmaps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visualization

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