Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems

Istepanian, R.S.H.; Philip, Nada; Martini, Maria G.

doi:10.1109/jsac.2009.090517

Cited by 74 publications

(62 citation statements)

References 31 publications

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“…When it comes to mobile health applications, medical video streaming via adaptive rate control algorithm was studied [158]. Reinforcement learning was applied to fulfill the requirement of high quality of service.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Reinforcement Learningmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…However, the growth rate in data storage and communication technologies is still lower than the rate of increase in demand for storage and transmission of medical data, in particular medical images and videos. Also, bandwidth is a precious resource in communication systems and compression is often needed before transmission, in particular over wireless systems [1], [2]. Hence, compressing images and videos is still a requirement for most applications and services, and the effect of compression and transmission is often a reduced quality.…”

Section: Introductionmentioning

confidence: 99%

A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

Razaak

Martini

Savino

2014

IEEE J. Biomed. Health Inform.

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Abstract-The Quality of Experience (QoE) and Quality of Service (QoS) provided in the healthcare sector are critical in evaluating the reliable delivery of the healthcare services provided. Medical images and videos play a major role in modern e-health services and have become an integral part of medical data communication systems. The quality evaluation of medical images and videos is an essential process, and one of the ways of addressing it is via the use of quality metrics. In this paper, we evaluate the performance of seven state of the art video quality metrics with respect to compressed medical ultrasound video sequences. We study the performance of each video quality metric in representing the diagnostic quality of the video, by evaluating the correlation of each metric with the subjective opinions of medical experts. The results indicate that the Visual Information Fidelity (VIF), Structural Similarity Index Metric (SSIM), and Universal Quality Index (UQI) metrics show good correlation with the subjective scores provided by medical experts. The tests also investigate the performance of the emerging video compression standard, High Efficiency Video Coding (HEVC), for medical ultrasound video compression. The results show that, using HEVC, a diagnostically reliable compressed ultrasound video can be obtained for compression with values of the quantization parameter, QP, upto 35.Index Terms-Medical video quality evaluation, HEVC, service science, objective & subjective video quality assessment, medical ultrasound videos, video compression.

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“…, (6) The R-square and Spearman rank goodness of fit values were above 0.9 for all studied cases as shown in Table 1. …”

Section: Comparison Of the Metrics On Logo And Main Frame With Varyinmentioning

confidence: 68%

“…Recently, there has been a large interest in electronic and mobile healthcare applications making use of advances in multimedia content processing and emerging high data rate wireless transmission standards [1][2][3][4][5][6]. Quality of Service / Quality of Experience (QoS/QoE) is an important aspect in assessing the validity and reliability of multimedia telemedicine applications [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Quality of Service / Quality of Experience (QoS/QoE) is an important aspect in assessing the validity and reliability of multimedia telemedicine applications [6][7][8][9][10][11]. There is therefore a need to develop efficient and accurate image and video quality assessment (IQA/VQA) methodologies that enable a physician or a medical specialist to have enough confidence to use the medical data (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A visual quality evaluation method for telemedicine applications

Nasr

Martini

2017

Signal Processing: Image Communication

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Ratio (PSNR) and the Structural SIMilarity index metric (SSIM). Comparisons with subjective results interms of Differential Mean Opinion Scores (DMOS) are also presented. We show that the presented method, not needing the original frame to predict the quality, achieves a high correlation with subjective results (more than 0.9) for the different quality metrics used. We also present relationships between the quality derived via the logo and via the original frame and we assess the overhead in data transmission resulting from the compressed logo data and its protection overhead.Index Terms-Visual quality evaluation, medical ultrasound, objective quality metrics, subjective quality metrics, medical quality of service/experience, wireless telemedicine

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Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems

Cited by 74 publications

References 31 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

A visual quality evaluation method for telemedicine applications

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