Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

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Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals

Frontiers in Physiology ◽

10.3389/fphys.2021.775052 ◽

2022 ◽

Vol 12 ◽

Author(s):

Silvia Seoni ◽

Simeon Beeckman ◽

Yanlu Li ◽

Soren Aasmul ◽

Umberto Morbiducci ◽

...

Keyword(s):

Logistic Regression ◽

Quality Assessment ◽

Real Time ◽

Template Matching ◽

Quality Metrics ◽

Laser Doppler ◽

Signal Quality ◽

Laser Doppler Vibrometry ◽

Quality Metric

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

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Coal quality prediction based on multi-feature fusion of flotation foam images

10.21203/rs.3.rs-21030/v1 ◽

2020 ◽

Author(s):

yateng bai ◽

xiaoping ma

Keyword(s):

Real Time ◽

Feature Fusion ◽

Image Features ◽

Image Feature ◽

Coal Preparation ◽

Coal Quality ◽

Coal Flotation ◽

Edge Operator ◽

Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

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Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology ◽

10.1109/iembs.2010.5626011 ◽

2010 ◽

Cited By ~ 14

Author(s):

M Bsoul ◽

H Minn ◽

M Nourani ◽

G Gupta ◽

L Tamil

Keyword(s):

Sleep Quality ◽

Quality Assessment ◽

Real Time ◽

Svm Classifier ◽

Multi Stage

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Coal quality prediction based on multi-feature fusion of flotation foam images

10.21203/rs.3.rs-29623/v1 ◽

2020 ◽

Author(s):

Yateng Bai ◽

Xiaoping Ma

Keyword(s):

Real Time ◽

Feature Fusion ◽

Image Features ◽

Image Feature ◽

Coal Preparation ◽

Coal Quality ◽

Coal Flotation ◽

Edge Operator ◽

Occurrence Matrix

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Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

Advances in Medical Technologies and Clinical Practice - Advancing the Investigation and Treatment of Sleep Disorders Using AI ◽

10.4018/978-1-7998-8018-9.ch007 ◽

2021 ◽

pp. 104-135

Author(s):

Sharad Sarjerao Jagtap ◽

Rajesh Kumar M.

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Wavelet Transform ◽

Real Time ◽

Low Cost ◽

Support Vector ◽

Svm Classifier ◽

Discrete Wavelet ◽

Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

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Ultrasound Medical Imaging Techniques

ACM Computing Surveys ◽

10.1145/3447243 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-38

Author(s):

Danilo Avola ◽

Luigi Cinque ◽

Alessio Fagioli ◽

Gianluca Foresti ◽

Alessio Mecca

Keyword(s):

Medical Imaging ◽

Real Time ◽

Imaging Techniques ◽

The Us ◽

High Level ◽

Surgical Operations ◽

Selection Of ◽

Us Technology

Ultrasound (US) imaging for medical purposes has been increasing in popularity over the years. The US technology has some valuable strengths, such as it is harmless, very cheap, and can provide real-time feedback. At the same time, it has also some drawbacks that the research in this field is trying to mitigate, such as the high level of noise and the low quality of the images. This survey aims at presenting the advances in the techniques used for US medical imaging. It describes the studies on the different organs that the US uses the most and tries to categorize the research in this field into three groups, i.e., segmentation, classification, and miscellaneous. This latter group includes the works that either provide aid during surgical operations or try to enhance the quality of the acquired US images/volumes. To the best of our knowledge, this is the first review that analyzes the different techniques exploited on a large selection of body locations (i.e., brain, thyroid, heart, breast, fetal, and prostate) in the three sub-fields of research.

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Towards Development of a Real-Time Point Feature Quality Assessment Method for Volunteered Geographic Information Using the Internet of Things

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030151 ◽

2021 ◽

Vol 10 (3) ◽

pp. 151

Author(s):

Sepehr Honarparvar ◽

Mohammad Reza Malek ◽

Sara Saeedi ◽

Steve Liang

Keyword(s):

Internet Of Things ◽

Quality Assessment ◽

Real Time ◽

Volunteered Geographic Information ◽

Assessment Method ◽

Geographic Information ◽

Positional Error ◽

Matched Data ◽

Quality Map

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time.

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QUANTIFYING THE QUALITY OF INDOOR MAPS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-739-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 739-745

Author(s):

M. Assali ◽

G. Pipelidis ◽

V. Podolskiy ◽

D. Iwaszczuk ◽

L. Heinen ◽

...

Keyword(s):

Quality Assessment ◽

Real Time ◽

Laser Scanning ◽

3D Models ◽

Building Maintenance ◽

Map Generation ◽

Feature Based ◽

And Robotics

<p><strong>Abstract.</strong> Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality.</p>

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Echtzeit-Bauteilprüfung beim Feinschneiden/Real-time quality analysis during fine blanking – Test rig for sheared part characterization using image processing and neural networks

wt Werkstattstechnik online ◽

10.37544/1436-4980-2020-06-22 ◽

2020 ◽

Vol 110 (06) ◽

pp. 382-388

Author(s):

Herman Voigts ◽

Rafael Hild ◽

Andreas Feuerhack ◽

Thomas Bergs

Keyword(s):

Neural Networks ◽

Image Processing ◽

Quality Assessment ◽

Real Time ◽

Quality Analysis ◽

Test Rig ◽

Fine Blanking ◽

Real Time Analysis ◽

Automated Image Processing

Die Schnittteilqualität beim Feinschneiden unterliegt einer Vielzahl von Einflussfaktoren. Derzeit findet die Qualitätsbewertung offline vom Prozess statt. Um eine echtzeitfähige Qualitätsbeurteilung für den Einsatz von Assistenzsystemen zu ermöglichen, wurde eine bildverarbeitende Methodik untersucht. Es wurde ein Prüfstand entwickelt zur Erforschung der Methoden für eine automatisierte bildverarbeitende und echtzeitfähige Analyse der Schnittteilqualität mittels neuronalen Netzen.   The quality of the sheared surface during fine blanking is subject to a large number of influencing factors. Currently, quality assessment is carried out offline. To enable real-time quality assessment based on assistance systems, an image-processing methodology was investigated. A test rig was developed to investigate methods for automated image processing and real-time analysis of the sheared surface quality by means   of neural networks.

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A technical review of no-reference image quality assessment algorithms for contrast distorted images

Journal of Engineering Research ◽

10.36909/jer.11885 ◽

2021 ◽

Author(s):

Preeti Mittal ◽

◽

Rajesh Kumar Saini ◽

Justin Varghese ◽

Neeraj Kumar Jain ◽

...

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Real Time ◽

Image Quality Assessment ◽

Human Vision ◽

Reference Image ◽

Benchmark Assessment ◽

Reference Images ◽

Insight Into

Automatic image quality assessment similar to human vision perception is an essential process for real-time image processing applications to perform perceptual image assessments for effectively achieving their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, these algorithms suit real-time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised. This study provides an insight into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.

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