Methods for interpreting pathological changes in the lun gs using computer tomography for diagnosing COVID-19

2021 ◽  
Vol 1 ◽  
pp. 14-23
Author(s):  
Konstantin Simonov ◽  
◽  
Anzhelika Kents ◽  
Yousif Hamad ◽  
Alexey Kruglyakov
Keyword(s):  
Geometric Analysis ◽  
Threshold Value ◽  
Clinical State ◽  
Pathological Changes ◽  
Color Coding ◽  
Remote Diagnostics ◽  
Algorithmic Support ◽  
Diagnostic Report ◽  
Combination Of Methods ◽  
Selection Of

Сomputed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks: highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the CT images. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.

scholarly journals METHODS AND MODELS FOR TEXTURE ANALYSIS OF LUNG PATHOLOGICAL CHANGES BASED ON COMPUTED TOMOGRAPHY FOR COVID-19 DIAGNOSIS

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 99-105
Author(s):  
A. S. Kents ◽  
Y. A. Hamad ◽  
K. V. Simonov ◽  
A. G. Zotin
Keyword(s):  
Computed Tomography ◽  
Geometric Analysis ◽  
Threshold Value ◽  
Clinical State ◽  
Pathological Changes ◽  
Color Coding ◽  
Algorithmic Support ◽  
Diagnostic Report ◽  
Combination Of Methods ◽  
Selection Of

Abstract. In recent years computed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks such as highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the images.

Approbation of the shearlet transformation method for visualization of pathological changes in the lungs on CT images for diagnosing COVID-19

2021 ◽  
Vol 3 ◽  
Author(s):  
А.S. Kents ◽  
◽  
Y.A. Hamad ◽  
K.V. Simonov
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Experimental Studies ◽  
Decomposition Methods ◽  
Transformation Method ◽  
Ct Images ◽  
Pathological Changes ◽  
Color Coding ◽  
Remote Diagnostics ◽  
Selection Of

Radiation diagnostics is a rapidly developing field of medicine which actively includes such concepts as artificial intelligence, computer vision and new methods of medical imaging. Given the urgency of the problem of the appearance of Covid-19 a methodology for processing, analyzing and interpreting CT images is proposed for the effective detection, texture analysis and visualization of pathological changes in the lungs with Covid-19. In the format of advances in AI and computer vision in diagnostics, combined in a new direction – radiomics which is based on the selection of a set of quantitative parameters of the pathology under study with the most accurate values of indicators (markers). Depending on the purpose of the medical research, the extracted features (markers) will differ. An analysis of textural features was carried out based on spectral decomposition methods (wavelet and shеarlet transform of images) with their contrasting with color coding. This approach makes it possible to more accurately assess the quantitative characteristics of the identified changes. As a result of experimental studies a presentation was formed for a medical specialist, followed by a final X-ray diagnostic conclusion. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.

Textural analysis of lung pathological changes using computer tomography for COVID-19 diagnostics

2021 ◽  
Vol 1 ◽  
pp. 24-40
Author(s):  
Konstantin Simonov ◽  
◽  
Anzhelika Kents ◽  
Yousif Hamad ◽  
Alexander Matsulev
Keyword(s):  
Spectral Decomposition ◽  
Respiratory Diseases ◽  
Effective Means ◽  
Geometric Analysis ◽  
Texture Features ◽  
Textural Analysis ◽  
Color Coding ◽  
Dynamic Changes ◽  
Remote Diagnostics ◽  
Computational Technology

The study is devoted to the development of computational technology (algorithms) for constructing models of texture analysis and visualization of images of computed tomography of the lungs as applied to the problem of diagnosing pathology associated with COVID-19. Within the framework of computational technology it is proposed to use algorithms for noise reduction, contrast enhancement, segmentation and spectral decomposition (shearlet transform). On this basis models of texture (geometric) analysis are proposed for highlighting and contrasting local objects of interest, taking into account the use of color coding for contrast. The analysis of dynamic changes in CT images of the lungs in the presence of changes associated with COVID-19 in patients with confirmed laboratory diagnostic data was performed. The results of the experimental study show that the developed computational technologies and the proposed models are effective means for quantitative analysis of the variability of the texture features of the studied images as well as for dynamic analysis over time and predicting possible states. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

scholarly journals Sistem Pendukung Keputusan Pemilihan Mobil Bekas Menggunakan Metode Analythical Hierarchy Process

2021 ◽  
Vol 3 (2) ◽  
pp. 380-386
Author(s):  
Gushelmi Gushelmi ◽  
Dodi Guswandi
Keyword(s):  
Selection Process ◽  
Threshold Value ◽  
Internet Technology ◽  
A Value ◽  
Used Cars ◽  
Hierarchy Process ◽  
Selection Of ◽  
Comparison Data

Showroom Ragasa Motor Padang is a showroom that sells various types of used cars. The old system of selecting used cars in The Ragasa Motor Padang Showroom is that customers come directly to the address of this Showroom and the selection process is still done by manual means. With the development of internet technology today is increasing rapidly and in order to be accessible to everyone, the AHP can do a comparison of the criteria in pairs on the selection of used cars and can determine the consistency of the comparison data paired with a threshold value of < 0.1. The purpose of this research is to make it easier for customers to choose used cars quickly and accurately, as well as the application of programs used to make it easier for customers to use them. The result of this study is the SPK System that was built to be able to take the decision of the selection of used cars in the Showroom Ragasa Motor Padang with the selection of the 2nd alternative with a value of 2.55 as the best choice.

scholarly journals Layer contour characterization in additive manufacturing through image binarization

2021 ◽  
Vol 1193 (1) ◽  
pp. 012067
Author(s):  
D Blanco ◽  
A Fernández ◽  
P Fernández ◽  
B J Álvarez ◽  
F Peña
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Threshold Value ◽  
Image Binarization ◽  
Actual Contour ◽  
Filter Size ◽  
Characterization Procedure ◽  
Flat Bed Scanner ◽  
Selection Of ◽  
Excellent Coverage

Abstract On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

Corrosion Protection of Construction Steel

2020 ◽  
pp. 327-347
Author(s):  
Arkadeb Mukhopadhyay ◽  
Sarmila Sahoo
Keyword(s):  
Carbon Dioxide ◽  
Corrosion Resistance ◽  
Corrosion Rate ◽  
Passive Film ◽  
Surface Engineering ◽  
Protective Coatings ◽  
Threshold Value ◽  
Construction Steel ◽  
Steel Rebars ◽  
Selection Of

Reinforced concrete is one of the most versatile materials for construction. In spite of this, the performance is limited by corrosion, cracking, and spalling of the steel rebars. The steel embedded in the concrete is protected by a passive film from the corrosive attack of chlorides, carbon dioxide, and sulphates. As the concentration of chlorides, carbon dioxide, or sulphates increases above a certain threshold value at the concrete rebar interface, the passive film breaks and leads to a severe increase in the corrosion rate. Further, dynamic loading and the temperature of the surroundings also affect the durability of the reinforcements. The rebar may be protected from such a corrosion attack by the suitable selection of material, improving the concrete quality and tailoring its composition or application of protective coatings. The present chapter highlights and summarizes the different grades of steel for their high corrosion resistance. Further, surface engineering and application of corrosion resistance coatings for the prevention of corrosion of construction steel rebars has been also discussed elaborately.

Photogrammetry: A Powerful Tool for Geometric Analysis of Solar Concentrators and Their Components

2005 ◽  
Vol 127 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Klaus Pottler ◽  
Eckhard Lu¨pfert ◽  
Glen H. G. Johnston ◽  
Mark R. Shortis
Keyword(s):  
Geometric Analysis ◽  
Solar Concentrator ◽  
Measurement Instruments ◽  
Solar Concentrators ◽  
Gravitational Forces ◽  
Surface Data ◽  
Efficient Measurement ◽  
Close Range ◽  
Coordinate Measurements ◽  
Selection Of

Digital close range photogrammetry has proven to be a precise and efficient measurement technique for the assessment of shape accuracies of solar concentrators and their components. The combination of high quality mega-pixel digital still cameras, appropriate software, and calibrated reference scales in general is sufficient to provide coordinate measurements with precisions of 1:50,000 or better. The extreme flexibility of photogrammetry to provide high accuracy 3D coordinate measurements over almost any scale makes it particularly appropriate for the measurement of solar concentrator systems. It can also provide information for the analysis of curved shapes and surfaces, which can be very difficult to achieve with conventional measurement instruments. The paper gives an overview of quality indicators for photogrammetric networks, which have to be considered during the data evaluation to augment the measurement precision. A selection of measurements done on whole solar concentrators and their components are presented. The potential of photogrammetry is demonstrated by presenting measured effects arising from thermal expansion and gravitational forces on selected components. The measured surface data can be used to calculate slope errors and undertake ray-trace studies to compute intercept factors and assess concentrator qualities.

scholarly journals Long-Term Monitoring of Atrazine Contamination in Soil by ELISA

2001 ◽  
Vol 84 (1) ◽  
pp. 150-155 ◽  
Author(s):  
Karl Kramer ◽  
Johann Lepschy ◽  
Bertold Hock
Keyword(s):  
Low Cost ◽  
Threshold Value ◽  
Soil Samples ◽  
Sample Throughput ◽  
Atrazine Contamination ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Selection Of ◽  
Hplc Data

Abstract An enzyme-linked immunoassay (ELISA) was used for screening atrazine residues in soil. Samples were annually collected in Southern Germany between 1993 and 1998. An average of 419.5 samples was analyzed per year amounting to 2517 samples. The fraction of positive samples defined by atrazine concentrations &gt;100 μg/kg soil decreased successively from 8% (corresponding to 33 samples) in 1993 to 0.6% (corresponding to 2 samples) in 1998. All positive samples and a selection of negative samples were subsequently validated by HPLC. Comparison of ELISA and HPLC data yielded correlation coefficient values of r= 0.958–0.981 (n= 18–47), except for 1995 when only a correlation of r= 0.864 (n= 18) was obtained. Four samples were overestimated and another 4 were underestimated with respect to the atrazine threshold value of 100 μg/kg soil as revealed by HPLC validation. Thus, 99.68% of 2517 analyzed samples were correctly evaluated. The precision and reproducibility of the ELISA were adequate for a prescreening tool. The low cost per sample and the high sample throughput are not yet achievable by conventional analytical methods. The described combination of ELISA and HPLC has the potential to take advantage of both methods and to restrict determination errors to a minimum.

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