Medical Image Compression
医学图像压缩
基本信息
- 批准号:171073-2013
- 负责人:
- 金额:$ 1.75万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2015
- 资助国家:加拿大
- 起止时间:2015-01-01 至 2016-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Prompt access to clinical information is crucial for improved healthcare decisions. The Electronic Health Record (EHR) promises better care by providing centralized access to the different sources of data that make up the patient's medical record. EHR is expected to help achieve a more efficient health-care system by enhancing productivity through timely access to information; it is also expected to help achieve a more effective system by reducing the duplication of tests. Medical images are very important information in the patient's health record. With Canada Health Infoway's investment in diagnostic imaging projects, medical images are centrally archived for easier long-term management. Images are also being shared and accessed by a very large number of healthcare professionals: by clinicians as part of a patient's follow-up and treatment; by radiologists to provide second opinions or first opinions when no radiologists are on site for trauma or remote-area cases; or by specialized physicians, such as surgeons or orthopedists, in planning surgical operations or procedures. The size of medical images is continuously growing. Computed Tomography (CT), for example, can generate a series of thousands of images whose size exceeds the gigabyte. Transferring large image sets on a communication network requires an enormous bandwidth, resulting in an inefficient and inadequate system. Images can be compressed with no information loss, reducing their size by 30%. Further compressing is needed, but currently available methods introduce artifacts and distortions that may impact diagnostic accuracy. In this research program, we propose to quantitatively assess the impact of compression methods on the diagnostic value of medical images. We also propose to develop novel compression schemes specially tailored to preserve that diagnostic value. Therefore, we expect to reduce the bandwidth needed to transfer medical images while preserving their diagnostic value.
及时获取临床信息对于改善医疗决策至关重要。电子健康记录(EHR)通过提供对构成患者医疗记录的不同数据源的集中访问,承诺提供更好的护理。预计电子健康记录将通过及时获取信息来提高生产力,从而帮助实现更有效率的保健系统;它还有望通过减少重复测试来帮助实现更有效的系统。医学图像是患者健康档案中非常重要的信息。随着加拿大Health Infoway对诊断成像项目的投资,医学图像被集中归档,以便更容易地进行长期管理。图像也被大量的医疗保健专业人员共享和访问:由临床医生作为患者后续和治疗的一部分;由放射科医生在没有放射科医生在创伤或偏远地区病例的现场时提供第二意见或第一意见;或由专业医生(如外科医生或骨科医生)在计划外科手术或程序时共享和访问。医学图像的大小不断增长。例如,计算机断层扫描(CT)可以生成一系列大小超过千兆字节的图像。在通信网络上传输大图像集需要巨大的带宽,导致系统效率低下且不充分。图像可以在不丢失信息的情况下进行压缩,将其大小减小了30%。需要进一步压缩,但当前可用的方法引入了可能影响诊断准确性的伪影和失真。在这项研究计划中,我们建议定量评估压缩方法对医学图像诊断价值的影响。我们还建议开发专门为保留这种诊断价值而量身定做的新的压缩方案。因此,我们希望减少传输医学图像所需的带宽,同时保留其诊断价值。
项目成果
期刊论文数量(0)
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专利数量(0)
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Noumeir, Rita其他文献
Inflight Broadband Connectivity Using Cellular Networks
- DOI:
10.1109/access.2016.2537648 - 发表时间:
2016-01-01 - 期刊:
- 影响因子:3.9
- 作者:
Tadayon, Navid;Kaddoum, Georges;Noumeir, Rita - 通讯作者:
Noumeir, Rita
Arterial Partial Pressures of Carbon Dioxide Estimation Using Non-Invasive Parameters in Mechanically Ventilated Children
- DOI:
10.1109/tbme.2020.3001441 - 发表时间:
2021-01-01 - 期刊:
- 影响因子:4.6
- 作者:
El Tannoury, Jihad;Sauthier, Michael;Noumeir, Rita - 通讯作者:
Noumeir, Rita
Infrared and 3D Skeleton Feature Fusion for RGB-D Action Recognition
- DOI:
10.1109/access.2020.3023599 - 发表时间:
2020-01-01 - 期刊:
- 影响因子:3.9
- 作者:
De Boissiere, Alban Main;Noumeir, Rita - 通讯作者:
Noumeir, Rita
Vision-Based Fall Detection Using ST-GCN
- DOI:
10.1109/access.2021.3058219 - 发表时间:
2021-01-01 - 期刊:
- 影响因子:3.9
- 作者:
Keskes, Oussema;Noumeir, Rita - 通讯作者:
Noumeir, Rita
Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study
- DOI:
10.1371/journal.pone.0198921 - 发表时间:
2019-02-20 - 期刊:
- 影响因子:3.7
- 作者:
Ghazal, Sam;Sauthier, Michael;Noumeir, Rita - 通讯作者:
Noumeir, Rita
Noumeir, Rita的其他文献
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{{ truncateString('Noumeir, Rita', 18)}}的其他基金
Decision support for the intensive care
重症监护决策支持
- 批准号:
RGPIN-2019-06205 - 财政年份:2022
- 资助金额:
$ 1.75万 - 项目类别:
Discovery Grants Program - Individual
Decision support for the intensive care
重症监护的决策支持
- 批准号:
RGPIN-2019-06205 - 财政年份:2021
- 资助金额:
$ 1.75万 - 项目类别:
Discovery Grants Program - Individual
An intelligent vision-based system for detecting self-harm behavior
用于检测自残行为的基于智能视觉的系统
- 批准号:
523637-2018 - 财政年份:2020
- 资助金额:
$ 1.75万 - 项目类别:
Collaborative Research and Development Grants
Decision support for the intensive care
重症监护的决策支持
- 批准号:
RGPIN-2019-06205 - 财政年份:2020
- 资助金额:
$ 1.75万 - 项目类别:
Discovery Grants Program - Individual
An intelligent vision-based system for detecting self-harm behavior
用于检测自残行为的基于智能视觉的系统
- 批准号:
523637-2018 - 财政年份:2019
- 资助金额:
$ 1.75万 - 项目类别:
Collaborative Research and Development Grants
Decision support for the intensive care
重症监护的决策支持
- 批准号:
RGPIN-2019-06205 - 财政年份:2019
- 资助金额:
$ 1.75万 - 项目类别:
Discovery Grants Program - Individual
An intelligent vision-based system for detecting self-harm behavior
用于检测自残行为的基于智能视觉的系统
- 批准号:
523637-2018 - 财政年份:2018
- 资助金额:
$ 1.75万 - 项目类别:
Collaborative Research and Development Grants
Medical Image Compression
医学图像压缩
- 批准号:
171073-2013 - 财政年份:2017
- 资助金额:
$ 1.75万 - 项目类别:
Discovery Grants Program - Individual
Intelligent surveillance for event detection
事件检测的智能监控
- 批准号:
498033-2016 - 财政年份:2016
- 资助金额:
$ 1.75万 - 项目类别:
Engage Plus Grants Program
Real-time telemedicine for emergency medical evacuation by air transportation
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- 批准号:
465644-2014 - 财政年份:2015
- 资助金额:
$ 1.75万 - 项目类别:
Collaborative Research and Development Grants
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