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.

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.

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.

The diagnosis of early pneumoconiosis in dust-exposed workers: comparison of chest radiography and computed tomography

2021 ◽  
pp. 028418512110225
Author(s):  
Hideyuki Hayashi ◽  
Kazuto Ashizawa ◽  
Masashi Takahashi ◽  
Katsuya Kato ◽  
Hiroaki Arakawa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Chest Radiography ◽  
Pulmonary Emphysema ◽  
Early Stage ◽  
Pathological Changes ◽  
Occupational History ◽  
Exposed Workers ◽  
History Of ◽  
Ct Data ◽  
Positive Results

Background Chest radiography (CR) is employed as the evaluation of pneumoconiosis; however, we sometimes encounter cases in which computed tomography (CT) is more effective in detecting subtle pathological changes or cases in which CR yields false-positive results. Purpose To compare CR to CT in the diagnosis of early-stage pneumoconiosis. Material and Methods CR and CT were performed for 132 workers with an occupational history of mining. We excluded 23 cases of arc-welder’s lung. Five readers who were experienced chest radiologists or pulmonologists independently graded the pulmonary small opacities on CR of the remaining 109 cases. We then excluded 37 cases in which the CT data were not sufficient for grading. CT images of the remaining 72 cases were graded by the five readers. We also assessed the degree of pulmonary emphysema in those cases. Results The grade of profusion on CR (CR score) of all five readers was identical in only 5 of 109 cases (4.6%). The CR score coincided with that on CT in 40 of 72 cases (56%). The CT score was higher than that on CR in 13 cases (18%). On the other hand, the CT score was lower than that on CR in 19 cases (26%). The incidence of pulmonary emphysema was significantly higher in patients whose CR score was higher than their CT score. Conclusion CT is more sensitive than CR in the evaluation of early-stage pneumoconiosis. In cases with emphysema, the CR score tends to be higher in comparison to that on CT.

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.

Imaging for approach selection of TAVI: assessment of the aorto-iliac tract diameter by computed tomography-angiography versus projection angiography

2013 ◽  
Vol 30 (2) ◽  
pp. 399-405 ◽  
Author(s):  
E. M. A. Wiegerinck ◽  
H. A. Marquering ◽  
N. Y. Oldenburger ◽  
M. A. Elattar ◽  
R. N. Planken ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Selection Of

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.

Sign in / Sign up

Export Citation Format

Share Document