CUQI: cardiac ultrasound video quality index

Razaak, Manzoor; Martini, Maria G.

doi:10.1117/1.jmi.3.1.011011

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…A comparison of Video Quality Assessment (VQA) metrics for HEVC in medical ultrasound was presented in [29,30]. The average QL MSSIM correlation results obtained above are comparable to the SSIM, the Video Information Fidelity (VIF) [31] and the Universal Quality Index (UQI) [32] metric results reported in [29] and better than VQM [33] metric results also reported in [29].…”

Section: Subjective Tests Investigations and Resultsmentioning

confidence: 59%

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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Section: Subjective Tests Investigations and Resultsmentioning

confidence: 59%

A visual quality evaluation method for telemedicine applications

Nasr

Martini

2017

Signal Processing: Image Communication

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“…• The double-stimulus continuous quality scale (DSCQS) where the observer is presented two stimuli side by side, typically the reference and the impaired one (Razaak and Martini 2016). • The subjective assessment for video quality (SAMVIQ) where the observer freely views and scores a collection of test stimuli associated with an explicitly identified reference stimulus (Rouse et al 2010).…”

Section: Subjective Test Methodologymentioning

confidence: 99%

Subjective and objective evaluations of feature selected multi output filter for speckle reduction on ultrasound images

Outtas¹,

Zhang²,

Déforges³

et al. 2018

Phys. Med. Biol.

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Ultrasonographic examination, either as visual inspection or quantitative analysis, is the most widely diagnostic resource. However, speckle noise is one of the drawbacks that makes it less effective than other medical imaging systems. Several speckle reduction methods often offer effective speckle reduction but generally suffer from oversmoothing, a blurring effect and a man-made appearance. In this paper, we propose a multi-output filter based on the multiplicative multiresolution decomposition (MOF-MMD). This multi-scale based method enables the enhancement of the original image and provides three enhanced outputs: global, edge and texture images. The multi-output filter aims at offering an enhanced image according to the features desired by radiologists. The different structures, textures and edges are filtered according to the contour image obtained by morphological operators. Three radiologists with different years of experience, have subjectively evaluated the speckle reduction methods according to enhanced features. The results of objective metrics and subjective evaluation showed that the proposed method reduces speckle and could help radiologists, according to their years of experience, in the diagnostic task. Finally, the correlation between three objective metrics and the perceived quality of contrast, diagnostic, texture and edges show that an objective metric is suitable for assessing quality of ultrasound images.

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“…An echocardiogram diagnostic system that is based on ML typically comprises automation of different tasks performed by human experts in the clinical investigation, as given in the flow diagram in Figure 1 . A considerable amount of work is performed on quality assessment [ 8 , 9 , 10 ] and view classification [ 5 , 11 , 12 , 13 , 14 ]. A vital component of the automated echocardiography pipeline is the delineation of the left ventricle in order to produce structural and functional indices using the traced boundaries.…”

Section: Related Workmentioning

confidence: 99%

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods

2023

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Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson’s method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

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CUQI: cardiac ultrasound video quality index

Cited by 8 publications

References 40 publications

A visual quality evaluation method for telemedicine applications

A visual quality evaluation method for telemedicine applications

Subjective and objective evaluations of feature selected multi output filter for speckle reduction on ultrasound images

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods

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