scholarly journals Paracoccidioidomycosis: level of pulmonary sequelae in high resolution computed tomography images from patients of two endemic regions of Brazil

2017 ◽  
Vol 7 (3) ◽  
pp. 318-325 ◽  
Author(s):  
Diana Rodrigues de Pina ◽  
Matheus Alvarez ◽  
Guilherme Giacomini ◽  
Ana Luiza Menegatti Pavan ◽  
Carlos Ivan Andrade Guedes ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
High Resolution Computed Tomography ◽  
Computed Tomography Images

scholarly journals Identification of emphysema patterns in high resolution computed tomography images

2017 ◽  
Vol 4 (1) ◽  
pp. 16
Author(s):  
Musibau A. Ibrahim ◽  
Oladotun A. Ojo ◽  
Peter A. Oluwafisoye
Keyword(s):  
Computed Tomography ◽  
Fractal Dimension ◽  
High Resolution ◽  
Hölder Exponent ◽  
High Resolution Computed Tomography ◽  
Box Counting ◽  
Computed Tomography Images ◽  
Local Intensity ◽  
Box Counting Method

Fractal dimension (FD) is a very useful metric for the analysis of image structures with statistically self-similar properties. It has applications in areas such as texture segmentation, shape classification and analysis of medical images. Several approaches can be used for calculating the fractal dimension of digital images; the most popular method is the box-counting method. It is also very challenging and difficult to classify patterns in high resolution computed tomography images (HRCT) using this important descriptor. This paper applied the Holder exponent computation of the local intensity values for detecting the emphysema patterns in HRCT images. The absolute differences between the normal and the abnormal regions in the images are the key for a successful classification of emphysema patterns using the statistical analysis. The results obtained in this paper demonstrated the effectiveness of the predictive power of the features extracted from the Holder exponent in the analysis and classification of HRCT images. The overall classification accuracy achieved in lung tissue layers is greater than 90%, which is an evidence to prove the effectiveness of the methods investigated in this paper.

scholarly journals Classification of High-Resolution Computed Tomography Manifestations for COVID-19 Patients: A Systematic Review

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Sanaz Alibabaei ◽  
Elham Rohollahpour ◽  
Marziyeh Tahmasbi
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
False Negative ◽  
Disease Process ◽  
Common Finding ◽  
High Resolution Computed Tomography ◽  
Computed Tomography Images ◽  
Peripheral Areas ◽  
Severity Of The Disease ◽  
Low Sensitivity

Context: The early detection of COVID-19 is of paramount importance for the disease treatment and control. As real-time reverse-transcription polymerase chain reaction indicates a low sensitivity, the computed tomography of patients' chest can play an effective role in the diagnosis of COVID-19, particularly for patients with false-negative RT-PCR tests. It is also effective in monitoring the clinical trends and assessing the severity of the disease. Objectives: Accordingly, this study aimed to review the different manifestations of the COVID-19 infections in High-Resolution Computed Tomography images of patients' chests and analyze the distribution of the disease in the lungs. The results can contribute to providing a comprehensive and concise reference on the appearance of various types of involvement and lung lesions and the extent of these lesions in the COVID-19 patients. Data Sources: We systematically searched four major indexing databases (namely PubMed, Science Direct, Google Scholar, and Cochrane Central) for articles published by May 2021 using the following keywords: High-Resolution Computed Tomography (HRCT), COVID-19, and Manifestations. Results: Overall, 29 studies addressing the role of HRCT in detecting and evaluating the manifestations of the COVID-19 infection in patients' lungs as Ground Glass Opacification (GGO), Consolidation, Irregular Solid Nodules, Fibrous Stripes, Crazy Paving Pattern, Air Bronchogram Sign, etc. were reviewed. Conclusions: GGO was the most common finding, as reported in 96.6% of the reviewed articles, followed by Consolidations (65.5%) and Irregular Solid Nodules (55.2%). Most patients revealed the disease process as a bilateral distribution in the peripheral areas of the lung.

scholarly journals Characteristics of chest high resolution computed tomography images of COVID-19: A retrospective study of 46 patients

2021 ◽  
Vol 37 (3) ◽  
Author(s):  
Yibo Lu ◽  
Jingru Zhou ◽  
Yimei Mo ◽  
Shulin Song ◽  
Xue Wei ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Retrospective Study ◽  
High Resolution ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Ground Glass ◽  
High Resolution Computed Tomography ◽  
Creative Commons ◽  
Computed Tomography Images ◽  
Clinical Records

Objective: To analyze the characteristics of chest high resolution computed tomography (CT) images of coronavirus disease 2019 (COVID-19). Methods: This is a retrospective study analyzing the clinical records and chest high-resolution CT images of 46 consecutive patients who were diagnosed with COVID-19 by nucleic acid tests and treated at our hospitals between January 2020 and February 2020. Results: Abnormalities in the CT images were found in 44 patients (95.6%). The lesions were unilateral in eight patients (17.4%), bilateral in 36 patients (78.3%), single in seven patients (15.9%), and multiple in 37 patients (84.1%). The morphology of the lesions was scattered opacity in 10 patients (21.7%), patchy opacity in 38 patients (82.6%), fibrotic cord in 17 patients (37.0%), and wedge-shaped opacity in two patients (4.3%). The lesions can be classified as ground-glass opacity in eight patients (17.4%), consolidation in one patient (2.2%), and ground-glass opacity plus consolidation in 28 patients (60.9%). Conclusion: Most COVID-19 patients showed abnormalities in chest CT images and the most common findings were ground-glass opacity plus consolidation. Abbreviations:COVID-19: coronavirus disease 2019, CT: computed tomography,SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, RNA: ribonucleic acid. doi: https://doi.org/10.12669/pjms.37.3.3504 How to cite this:Lu Y, Zhou J, Mo Y, Song S, Wei X, Ding K. Characteristics of Chest high resolution computed tomography images of COVID-19: A retrospective study of 46 patients. Pak J Med Sci. 2021;37(3):---------. doi: https://doi.org/10.12669/pjms.37.3.3504 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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