Porosity measurements by X-ray computed tomography: Accuracy evaluation using a calibrated object

Hermanek, Petr; Carmignato, Simone

doi:10.1016/j.precisioneng.2017.03.007

Cited by 62 publications

(26 citation statements)

References 15 publications

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“…There are three common methods to diagnose COVID-19, which are blood test, X-ray, and computed tomography (CT) scan [ 1 , 2 ]. Among many medical imaging technologies, CT is a non-invasive technology, that has been chosen since it is regarded as a promising technique for advanced internal porosity detection and characterization [ 3 ]. According to Zonneveld [ 4 ], CT technology plays an important role in diagnostic medicine, image-guided intervention, and the assessment of therapeutic and surgical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan

Al-Jawad

Jalab

et al. 2020

Entropy

142

105

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Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

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Section: Introductionmentioning

confidence: 99%

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan

Al-Jawad

Jalab

et al. 2020

Entropy

142

105

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“…New non-destructive techniques (NDT) [1] as well as volumetric techniques have to be investigated. X-ray tomography is presently the most appropriate technique [2][3][4] considering that it is a non-contact technique providing a high spatial resolution. However, it is a costly and time-consuming technique which may be unsuitable for mass production.…”

Section: Introductionmentioning

confidence: 99%

Investigation of new volumetric non-destructive techniques to characterise additive manufacturing parts

Obaton

Lê²,

Prezza³

et al. 2018

Weld World

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The assessment of the potential of various non-destructive methods to characterise additive manufactured dense and lattice parts was investigated. The Archimedes' and gas pycnometric methods for density measurements of both types of structures are presented as well as the percentage of cells in lattice structures. A multi-frequency eddy current method for electrical conductivity measurements of lattice structures is also presented. Finally, C-scan ultrasound method for the characterisation of dense parts was investigated. The advantages and limitations of each method are underlined.

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“…In recent years, 3D imaging by means of Computed Tomography (CT) has emerged as a new technology for industrial quality control in many industrial applications [7]. The main metrological capability of this non-contact of 17 imaging technique is based on the possibility of acquiring a densely populated 3D scanning point cloud of an object, allowing the measuring of free-form surfaces [8], non-accessible internal structures [9,10] and even multi-material components [11][12][13]. Therefore, regarding 3D complex surface geometries, Computed Tomography has the potential to become a viable solution for their dimensional measuring.…”

Section: Introductionmentioning

confidence: 99%

Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements

Torralba

Jiménez

Yagüe‐Fabra

et al. 2018

Int J Adv Manuf Technol

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The number of industrial applications of Computed Tomography (CT) for dimensional metrology in 10 0-10 3 mm range has been continuously increasing, especially in the last years. Due to its specific characteristics Computed Tomography has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e. in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination; while the second one is based on a gradient or discontinuity analysis: the 3D Canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D Canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements.

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Porosity measurements by X-ray computed tomography: Accuracy evaluation using a calibrated object

Cited by 62 publications

References 15 publications

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Investigation of new volumetric non-destructive techniques to characterise additive manufacturing parts

Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements

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