Advances in Imaging from the First X-Ray Images

Mikla, Victor I.; Mikla, Victor V.

doi:10.1016/b978-0-12-417021-6.00001-0

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The aim of the present report is to highlight some of the issues, methods, and obtained results in utilizing both machine vision methods and AI in approaching the problem of false positive results in the quality control of the above-mentioned products. This is in line with a larger direction of work in our group in collaboration with our industrial partners, regarding product quality assessment using a range of imaging methods, including scanning electron microscopy [1], Xray imaging [2], micro-CT [3], optical coherence tomography (OCT) [4][5][6][7], and confocal microscopy [8]. Applications approached so far included metallic fractures [9,10], electronic boards [11], polymers [12], as well as dental materials of different types [13][14][15][16][17][18][19].…”

Section: Introductionsupporting

confidence: 64%

Utilizing machine learning and AI algorithms for inspection of airbags manufacturing processes

Negrei,

Duma

2023

Digital Optical Technologies 2023

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Manufacturing processes of airbags products involve different and complex phases. An important one is the quality check using optical metrology methods both during the process and in its final phase. The aim of the present work is to study the advantages and limitations of utilizing a machine vision system for the above purposes. Thus, we report the development of a machine vision system for inter-phase and final quality check related to shape, dimensions, patterns, holes diameter, orientation, as well as the right sequence of processing airbag components. We utilized Basler/Cognex cameras with machine learning (ML)/artificial intelligence (AI) algorithms for inspection and CO2 lasers for cutting the components. Also, we developed (and further on optimized) in-house processes such as ultrasonic welding and dynamic positioning of components using cameras-guided robots. In order to validate the concept of the studies, several methods and tools have been utilized: virtual simulations programing tool (RobotStudio), CAD 3D mechanical design program (SolidWorks), Design of Experiments (DOE), Statistical Process Control (SPC), problem solving DMAIC (Define, Measure, Analyze, Improve, Control), as well as Process Failure Mode & Effects Analysis (PFMEA). The initial rejection rates (i.e., before applying these methods) were >30%, including false rejections. Overall, we reached an accuracy of 0.5 mm for dimensional measurements for textiles parts with different shapes in a field of view of 300 to 400 mm; rejection rate and falls rejects <1.3%; an appropriate stability and repeatability of the manufacturing process.

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Section: Introductionsupporting

confidence: 64%

Utilizing machine learning and AI algorithms for inspection of airbags manufacturing processes

Negrei,

Duma

2023

Digital Optical Technologies 2023

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“…The difference between units available on the market is made by the characteristics of the sensors, such as spatial resolution or contrast. The most utilized types of digital detectors in dental imaging are charge-coupled devices (CCDs), complementary metal oxide-semiconductors (CMOS), and flat panel sensors [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography

Erdelyi

Duma

Sinescu

et al. 2021

Sensors

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The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray units imaging (i.e., in terms of image resolution, radiation dose, or contrast). Two types of commercially available and widely used X-ray units are considered. To adjust their parameters, a protocol is developed to employ OCT images of dental conditions that are documented on high (i.e., less than 10 μm) resolution OCT images (both B-scans/cross sections and 3D reconstructions) but are hardly identified on the 200 to 75 μm resolution panoramic or CBCT radiographs. The optimized calibration of the X-ray unit includes choosing appropriate values for the anode voltage and current intensity of the X-ray tube, as well as the patient’s positioning, in order to reach the highest possible X-rays resolution at a radiation dose that is safe for the patient. The optimization protocol is developed in vitro on OCT images of extracted teeth and is further applied in vivo for each type of dental investigation. Optimized radiographic results are compared with un-optimized previously performed radiographs. Also, we show that OCT can permit a rigorous comparison between two (types of) X-ray units. In conclusion, high-quality dental images are possible using low radiation doses if an optimized protocol, developed using OCT, is applied for each type of dental investigation. Also, there are situations when the X-ray technology has drawbacks for dental diagnosis or treatment assessment. In such situations, OCT proves capable to provide qualitative images.

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“…Time is crucial in this serious situation, and one cannot wait for an unexpected spread of infection. Therefore, experts are already recommending the use of radiological imaging techniques such as X-rays [11]. X-rays play an important role in the field of experimental medical science.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Alqahtani¹,

Akram²,

Ramzan³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. However, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specialist in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

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Advances in Imaging from the First X-Ray Images

Cited by 6 publications

References 11 publications

Utilizing machine learning and AI algorithms for inspection of airbags manufacturing processes

Utilizing machine learning and AI algorithms for inspection of airbags manufacturing processes

Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

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