scholarly journals Accuracy of Panoramic Dental X-Ray Imaging in Detection of Proximal Caries with Multiple Morpological Gradient (mMG) Method

2017 ◽  
Vol 1 (1) ◽  
pp. 5
Author(s):  
Jufriadif Na`am
Keyword(s):  
Dental Caries ◽  
Tooth Decay ◽  
Occlusal Caries ◽  
Radiographic Images ◽  
Proximal Caries ◽  
X Ray ◽  
Early Handling ◽  
X Ray Imaging

Dental caries is tooth decay caused by bacterial infection. This is commonly known as tooth decay. Classification of caries by location consists of; occlusal caries, proximal caries, root caries and caries enamel. Diagnosis of dental caries in general carried out with the help of radiographic images is called Dental X-Ray. Dental X-Ray consists of bitewing, Periapical and Panoramic. Identification of proximal caries using Dental Panoramic X-Ray lowest precision was compared with both other Dental X-Ray. This study aims to perform sharpening and improving the quality of information contained in the image of Panoramic Dental X-Ray to clarify the edges of the objects contained in the image, making it easier to identify and proximal caries severity. The methods and algorithms used are multiple Morphology Gradient (mMG). The results obtained are increased accuracy in identifying proximal caries 47.5%. Based on the severity of it, that level of enamel = 47.37%; dentin rate = 42.1% and the rate of dentin = 1.3%. Accuracy level of accuracy in identifying proximal caries a higher level of email, so that patients with proximal caries early levels can be tackled early handling by the dentist

scholarly journals Anexvivocomparative study of occlusal and proximal caries using terahertz and X-ray imaging

2019 ◽  
Vol 48 (2) ◽  
pp. 20180250 ◽  
Author(s):  
Kıvanç Kamburoğlu ◽  
Burcu Karagöz ◽  
Hakan Altan ◽  
Doĝukan Özen
Keyword(s):  
Proximal Caries ◽  
X Ray ◽  
X Ray Imaging

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Imaging Performance of a Multimodal Module to Enhance Preclinical Irradiator Capabilities

2020 ◽  
pp. 1-4
Author(s):  
Sophie Pinel ◽  
Joël Daouk ◽  
Justine Jubréaux ◽  
Alicia Chateau ◽  
Hervé Schohn ◽  
...  
Keyword(s):  
Performance Measurements ◽  
Radiographic Images ◽  
Image Guided Radiotherapy ◽  
X Ray ◽  
Optical Images ◽  
X Ray Imaging ◽  
Single Sensor ◽  
Overall Performance ◽  
Imaging Performance ◽  
Image Homogeneity

This article highlights the performance measurements of an optical device which aims at upgrading preclinical irradiators. The evaluated device allows acquiring X-ray as well as bioluminescence images with a single sensor. The latter consists of a supercooled camera equipped with a 1024x1024 charge coupling device (each element measuring 13x13µm²). X-ray imaging is feasible, thanks to a conversion phosphor screen. Phantom acquisitions revealed a spatial resolution of 2.5 line pairs per millimetre (0.2mm) for Xray imaging and between 0.4 and 0.7mm for bioluminescence images. Image homogeneity was 0.8 for radiographic images with preclinical imaging parameters and higher than 0.9 for optical images. For functional imaging, contrast to noise ratio (CNR) ranged from 1.3 (for contrast of 2:1 and 0.1s acquisition) up to 253 (for contrast of 32:1 and 5s acquisition). CNR was related to acquisition duration. The device’s overall performance revealed that it is suitable to upgrade existing irradiators and improve laboratory capabilities toward image-guided radiotherapy.

Rapid, Non-destructive Inspection and Classification of Inhalation Blisters Using Low-Energy X-ray Imaging

2018 ◽  
Vol 13 (3) ◽  
pp. 270-282 ◽  
Author(s):  
Nagaraja Rao ◽  
Brian Ament ◽  
Richard Parmee ◽  
Jonathan Cameron ◽  
Martin Mayo
Keyword(s):  
Low Energy ◽  
X Ray ◽  
X Ray Imaging ◽  
Non Destructive

scholarly journals NuSTAR reveals that the heavily obscured nucleus of NGC 2785 was the contaminant of IRAS 09104+4109 in the BeppoSAX/PDS hard X-rays

2018 ◽  
Vol 619 ◽  
pp. A16
Author(s):  
C. Vignali ◽  
P. Severgnini ◽  
E. Piconcelli ◽  
G. Lanzuisi ◽  
R. Gilli ◽  
...  
Keyword(s):  
X Rays ◽  
X Ray ◽  
Star Forming ◽  
Physical Description ◽  
Enhanced Sensitivity ◽  
X Ray Imaging ◽  
First Time ◽  
In Virtue Of

Context. The search for heavily obscured active galactic nuclei has been revitalized in the last five years by NuSTAR, which has provided a good census and spectral characterization of a population of such objects, mostly at low redshift, thanks to its enhanced sensitivity above 10 keV compared to previous X-ray facilities, and its hard X-ray imaging capabilities. Aims. We aim at demonstrating how NGC 2785, a local (z = 0.009) star-forming galaxy, is responsible, in virtue of its heavily obscured active nucleus, for significant contamination in the non-imaging BeppoSAX/PDS data of the relatively nearby (≈17′) quasar IRAS 09104+4109 (z = 0.44), which was originally mis-classified as Compton thick. Methods. We analyzed ≈71 ks NuSTAR data of NGC 2785 using the MYTorus model and provided a physical description of the X-ray properties of the source for the first time. Results. We found that NGC 2785 hosts a heavily obscured (NH ≈ 3 × 1024 cm−2) nucleus. The intrinsic X-ray luminosity of the source, once corrected for the measured obscuration (L2−10keV ≈ 1042 erg s−1), is consistent within a factor of a few with predictions based on the source mid-infrared flux using widely adopted correlations from the literature. Conclusions. Based on NuSTAR data and previous indications from the Neil Gehrels Swift Observatory (BAT instrument), we confirm that NGC 2785, because of its hard X-ray emission and spectral shape, was responsible for at least one third of the 20–100 keV emission observed using the PDS instrument onboard BeppoSAX, originally completely associated with IRAS 09104+4109. Such emission led to the erroneous classification of this source as a Compton-thick quasar, while it is now recognized as Compton thin.

scholarly journals Parameters based on X-ray images to assess the physical and physiological quality of Leucaena leucocephala seeds

2018 ◽  
Vol 42 (6) ◽  
pp. 643-652 ◽  
Author(s):  
André Dantas de Medeiros ◽  
Joyce de Oliveira Araújo ◽  
Manuel Jesús Zavala León ◽  
Laércio Junio da Silva ◽  
Denise Cunha Fernandes dos Santos Dias
Keyword(s):  
Leucaena Leucocephala ◽  
High Performance ◽  
Principal Component ◽  
Automated Analysis ◽  
Radiographic Images ◽  
X Ray ◽  
Physiological Quality ◽  
Non Destructive ◽  
Do So

ABSTRACT Non-destructive and high performance analyses are highly desirable and important for assessing the quality of forest seeds. The aim of this study was to relate parameters obtained from semi-automated analysis of radiographs of Leucaena leucocephala seeds to their physiological potential by means of multivariate analysis. To do so, seeds from five lots collected from parent trees from the region of Viçosa, MG, Brazil, were used. The study was carried out through analysis of radiographic images of seeds, from which the percentage of damaged seeds (predation and fungi), and measurements of area, perimeter, circularity, relative density, and integrated density of the seeds were obtained. After the X-ray test, the seeds were tested for germination in order to assess variables related to seed physiological quality. Multivariate statistics were applied to the data generated, with use of principal component analysis (PCA). X-ray testing allowed visualization of details of the internal structure of seeds and differences regarding density of seed tissues. Semi-automated analysis of radiographic images of Leucaena leucocephala seeds provides information on seed physical characteristics and generates parameters related to seed physiological quality in a simple, fast, and inexpensive manner.

Validation of radiographic quality of simulation software - ImaSim

2021 ◽  
pp. 1-10
Author(s):  
Zhonghang Wu ◽  
Jieying Yu ◽  
Qianqing Wu ◽  
Pengfei Hou ◽  
Jiuai Sun
Keyword(s):  
Spatial Resolution ◽  
Simulation Software ◽  
Virtual Simulation ◽  
Radiographic Imaging ◽  
Proof Of Concept ◽  
X Ray ◽  
X Ray Imaging ◽  
Radiographic Contrast ◽  
Exposure Setting

BACKGROUND: Virtual radiographic simulation has been found educationally effective for students to practice their clinical examinations remotely or online. A free available virtual simulator-ImaSim has received particular attention for radiographic science education because of its portability, free of charge and no constrain of location and physical facility. However, it lacks evidence to validate this virtual simulation software to faithfully reproduce radiographs comparable to that taken from a real X-ray machines to date. OBJECTIVE: To evaluate imaging quality of the virtual radiographs produced by the ImaSim. Thus, the deployment of this radiographic simulation software for teaching and experimental studying of radiography can be justified. METHODS: A real medical X-ray examination machine is employed to scan three standard QC phantoms to produce radiographs for comparing to the corresponding virtual radiographs generated by ImaSim software. The high and low range of radiographic contrast and comprehensive contrast-detail performance are considered to characterize the radiographic quality of the virtual simulation software. RESULTS: ImaSim software can generate radiographs with a contrast ranging from 30% to 0.8% and a spatial resolution as low as 0.6mm under the selected exposure setting condition. The characteristics of contrast and spatial resolution of virtual simulation generally agree with that of real medical X-ray examination machine. CONCLUSION: ImaSim software can be used to simulate a radiographic imaging process to generate radiographs with contrast and detail detectability comparable to those produced by a real X-ray imaging machine. Therefore, it can be adopted as a flexible educational tool for proof of concept and experimental design in radiography.

In vitro comparison of subjective image quality of the pana digital intraoral x-ray imaging system and conventional intraoral radiography in caries detection

1998 ◽  
Vol 14 (2) ◽  
pp. 75-83 ◽  
Author(s):  
Yoshiko Ariji ◽  
Jin-ichi Takahashi ◽  
Osamu Matsui ◽  
Tsuneichi Okano ◽  
Munetaka Naitoh ◽  
...  
Keyword(s):  
Image Quality ◽  
Imaging System ◽  
Caries Detection ◽  
Intraoral Radiography ◽  
Subjective Image Quality ◽  
X Ray ◽  
X Ray Imaging ◽  
In Vitro Comparison

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Physical and physiological quality of Jatropha curcas L. seeds at different maturity stages using image analysis

2020 ◽  
Vol 18 (3) ◽  
pp. e0206
Author(s):  
Daniel T. Pinheiro ◽  
André D. Medeiros ◽  
Manuel J. Zavala-León ◽  
Denise C. F. S. Dias ◽  
Laércio J. Da Silva
Keyword(s):  
Image Analysis ◽  
Jatropha Curcas ◽  
Physical Characteristics ◽  
Radiographic Images ◽  
Seed Filling ◽  
X Ray ◽  
Tissue Integrity ◽  
Maturity Stages ◽  
Physiological Quality

Aim of study: To assess the potential of automated X-ray image analysis to evaluate the physical characteristics of Jatropha curcas seeds, and to relate the parameters obtained with the physiological quality of the seeds harvested at different maturity stages.Area of study: Experimental area of Agronomy Department, Federal University of Viçosa (UFV), Brazil.Material and methods: The fruits were harvested from 20 plants, based on the external skin color (green, yellow, brownish-yellow and brown). The study was performed by automated and visual analysis of radiographic images of the seeds, in which measurements of tissue integrity, density and seed filling were performed. Seed dry matter, germination and seedling growth were also analysed.Main results: Variables obtained through automated analysis of radiographic images correlated significantly with all physiological variables (r > 0.9), as well as visual image evaluations (r > 0.75). The seeds extracted from green fruits presented lower tissue integrity and lower physiological quality. Radiographic analysis was efficient for monitoring J. curcas seed quality at different maturity stages. Morpho-anatomical parameters obtained from X-ray analysis were highly correlated with seed physiological attributes.Research highlights: It is important to develop and improve methodologies based on lower-cost techniques, such as X-ray analysis. In this context, we verified that X-ray images can be used for monitoring J. curcas seed filling and maturation. Radiographic images of seeds can be analyzed automatically with ImageJ software. Internal morphology and physical characteristics of seeds have relationship with their physiological quality.

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