Medical image and video quality assessment in e-health applications and services

Razaak, Manzoor; Martini, Maria G.

doi:10.1109/healthcom.2013.6720628

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…Blur kernels might be estimated as a matrix of values [ 7 , 9 , 10 , 12 – 15 , 19 – 24 , 30 ]. These methods are mostly based on solving an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Javaran

Hassanpour

2021

Computational and Mathematical Methods in Medicine

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Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

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“…Blur kernels might be estimated as a matrix of values [ 7 , 9 , 10 , 12 – 15 , 19 – 24 , 30 ]. These methods are mostly based on solving an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Javaran

Hassanpour

2021

Computational and Mathematical Methods in Medicine

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“…In this paper, we delve into the topic of VQA in the context of medical ultrasound videos, extending the work presented in [5], where an insight into the VQA topic in the context of medical videos is given. Video quality evaluation techniques are mainly classified into objective and subjective methods.…”

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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“…1 The process of video quality evaluation can be of two types: "subjective" and "objective" quality evaluation. To overcome this demand, video compression is often used.…”

Section: Introductionmentioning

confidence: 99%

CUQI: cardiac ultrasound video quality index

Razaak

Martini

2016

J. Med. Imag

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Medical images and videos are now increasingly part of modern telecommunication applications, including telemedicinal applications, favored by advancements in video compression and communication technologies. Medical video quality evaluation is essential for modern applications since compression and transmission processes often compromise the video quality. Several state-of-the-art video quality metrics used for quality evaluation assess the perceptual quality of the video. For a medical video, assessing quality in terms of "diagnostic" value rather than "perceptual" quality is more important. We present a diagnostic-quality-oriented video quality metric for quality evaluation of cardiac ultrasound videos. Cardiac ultrasound videos are characterized by rapid repetitive cardiac motions and distinct structural information characteristics that are explored by the proposed metric. Cardiac ultrasound video quality index, the proposed metric, is a full reference metric and uses the motion and edge information of the cardiac ultrasound video to evaluate the video quality. The metric was evaluated for its performance in approximating the quality of cardiac ultrasound videos by testing its correlation with the subjective scores of medical experts. The results of our tests showed that the metric has high correlation with medical expert opinions and in several cases outperforms the state-of-the-art video quality metrics considered in our tests.

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Medical image and video quality assessment in e-health applications and services

Cited by 9 publications

References 32 publications

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Using a Blur Metric to Estimate Linear Motion Blur Parameters

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

CUQI: cardiac ultrasound video quality index

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