Large-scale PACS implementation strategies: image data acquisition and distribution services

1994 ◽  
Author(s):  
Lu J. Huang ◽  
Daniel J. Valentino ◽  
Ricky K. Taira
Keyword(s):  
Data Acquisition ◽  
Large Scale ◽  
Implementation Strategies ◽  
Image Data ◽  
Pacs Implementation ◽  
Distribution Services

The Application of 3D Laser Scanning Technology in Ancient Architecture Protection

2013 ◽  
Vol 353-356 ◽  
pp. 3476-3479
Author(s):  
Jun Lan Zhao ◽  
Ran Wu ◽  
Lei Wang ◽  
Yi Qin Wu
Keyword(s):  
3D Reconstruction ◽  
Data Acquisition ◽  
High Speed ◽  
Laser Scanning ◽  
Large Scale ◽  
High Efficiency ◽  
Image Data ◽  
High Accuracy ◽  
3D Laser Scanning ◽  
3D Data

The study of 3D laser scanning technology in Category Conservation is one of the hot researches in recent years. Through the high-speed laser scanning, catching the 3D data of an object in large-scale with high efficiency, high accuracy and excellent resolution, is a new way in 3D reconstruction and image data acquisition. The method has achieved good results through the experiment.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

scholarly journals NOAA Image Data Acquisition to Determine Soil Moisture in Arequipa-Perú

2021 ◽  
pp. 1-1
Author(s):  
A. Argume ◽  
R. Coaguila ◽  
P.R. Yanyachi ◽  
J. Chilo
Keyword(s):  
Soil Moisture ◽  
Data Acquisition ◽  
Image Data

scholarly journals System design for inferring colony-level pollination activity through miniature bee-mounted sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haron M. Abdel-Raziq ◽  
Daniel M. Palmer ◽  
Phoebe A. Koenig ◽  
Alyosha C. Molnar ◽  
Kirstin H. Petersen
Keyword(s):  
Data Acquisition ◽  
Honey Bees ◽  
Large Scale ◽  
Physical World ◽  
Cluttered Environments ◽  
Robotic Mapping ◽  
Large Scale Data ◽  
Trade Offs ◽  
Large Numbers ◽  
Engineered Systems

AbstractIn digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

scholarly journals Exploring the feasibility of a network of organizations for pain rehabilitation: what are the lessons learned?

2020 ◽  
Author(s):  
Cynthia Lamper ◽  
Ivan PJ Huijnen ◽  
Mariëlle EAL Kroese ◽  
Albère J Köke ◽  
Gijs Brouwer ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Large Scale ◽  
Critical Mass ◽  
Implementation Strategies ◽  
Daily Practice ◽  
Lessons Learned ◽  
Integration Of Care ◽  
Treatment Protocols ◽  
Rehabilitation Care

Abstract Background and aims: Integration of care is lacking for chronic musculoskeletal pain (CMP) patients. Network Pain Rehabilitation Limburg (NPRL), a transmural health care network, has been designed to provide integrated rehabilitation care from a biopsychosocial perspective to improve patients’ levels of functioning. This feasibility study aims to provide insight into barriers and facilitators for the development, implementation, and transferability of NPRL.Methods: This study was conducted with a three-phase iterative and incremental design from October 2017 to October 2018. NPRL comprises two rehabilitation practices, and three local primary care networks, with a general practitioner together with, a mental health practice nurse, and a physiotherapist or exercise therapist. These stakeholders with a random sample of participating patients took part in evaluations, consisting of interviews, focus groups, and observations. Field notes and observations were recorded during meetings. The Consolidated Framework for Implementation Research guided data collection and analysis. Results were used to refine the next phase.Results: According to health care professionals (HCPs), guidelines and treatment protocols facilitate consistency and transparency in collaboration, biopsychosocial language, and treatment. One barrier is stigmatization of CMP in society. Non-participating HCPs’ treatment approaches are often more biomedical than biopsychosocial, causing patients to resist participating in NPRL. The current organization of health care, with cultural, structural, and financial aspects, acts as a barrier, complicating implementation between and within practices. HCPs preferred the iterative, bottom-up strategy. A critical mass of participating organizations is needed for proper implementation.Conclusion: NPRL is feasible in daily practice if barriers are overcome and facilitators of development, implementation, and transferability are promoted. These findings will be used to refine NPRL. A large-scale process and effect evaluation will be performed. Our implementation strategies and results may assist other health care organizations aspiring to implement a transmural network using a similar model.

scholarly journals A Systematic Analysis of Big Image Data Methodologies in Various Applications

2020 ◽  
Vol 9 (5) ◽  
pp. 483-487
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Scale ◽  
Image Data ◽  
Computational Time ◽  
Process Data ◽  
Systematic Analysis ◽  
Large Scale Data ◽  
Learning Techniques ◽  
Effective Performance

Big data is large-scale data collected for knowledge discovery, it has been widely used in various applications. Big data often has image data from the various applications and requires effective technique to process data. In this paper, survey has been done in the big image data researches to analysis the effective performance of the methods. Deep learning techniques provides the effective performance compared to other methods included wavelet based methods. The deep learning techniques has the problem of requiring more computational time, and this can be overcome by lightweight methods.

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