The Use of a Slice Feature Vector of Classifying Diffuse Lung Opacities in High-Resolution Computed Tomography Images

2012 ◽  
Author(s):  
Yoshihiro Mitani ◽  
Yusuke Fujita ◽  
Naofumi Matsunaga ◽  
Yoshihiko Hamamoto
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Feature Vector ◽  
High Resolution Computed Tomography ◽  
Computed Tomography Images ◽  
Diffuse Lung

Development and evaluation of a computer-based decision support system for diffuse lung diseases at high-resolution computed tomography

2021 ◽  
pp. 028418512199579
Author(s):  
Simon S Martin ◽  
Delina Kolaneci ◽  
Julian L Wichmann ◽  
Lukas Lenga ◽  
Doris Leithner ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Decision Support ◽  
High Resolution ◽  
Lung Diseases ◽  
Differential Diagnoses ◽  
High Resolution Computed Tomography ◽  
Computer Based ◽  
Diffuse Lung Diseases ◽  
Hrct Patterns ◽  
Diffuse Lung

Background High-resolution computed tomography (HRCT) is essential in narrowing the possible differential diagnoses of diffuse and interstitial lung diseases. Purpose To investigate the value of a novel computer-based decision support system (CDSS) for facilitating diagnosis of diffuse lung diseases at HRCT. Material and Methods A CDSS was developed that includes about 100 different illustrations of the most common HRCT signs and patterns and describes the corresponding pathologies in detail. The logical set-up of the software facilitates a structured evaluation. By selecting one or more CT patterns, the program generates a ranked list of the most likely differential diagnoses. Three independent and blinded radiology residents initially evaluated 40 cases with different lung diseases alone; after at least 12 weeks, observers re-evaluated all cases using the CDSS. Results In 40 patients, a total of 113 HRCT patterns were evaluated. The percentage of correctly classified patterns was higher with CDSS (96.8%) compared to assessment without CDSS (90.3%; P < 0.01). Moreover, the percentage of correct diagnosis (81.7% vs. 64.2%) and differential diagnoses (89.2% vs. 38.3%) were superior with CDSS compared to evaluation without CDSS (both P < 0.01). Conclusion Addition of a CDSS using a structured approach providing explanations of typical HRCT patterns and graphical illustrations significantly improved the performance of trainees in characterizing and correctly identifying diffuse lung diseases.

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 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

Reader accuracy and confidence in diagnosing diffuse lung disease on high-resolution computed tomography of the lungs: impact of sampling frequency

2008 ◽  
Vol 49 (8) ◽  
pp. 870-875 ◽  
Author(s):  
B. Sundaram ◽  
B. H. Gross ◽  
E. Oh ◽  
N. Müller ◽  
J. D. Myles ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Disease ◽  
Sampling Frequency ◽  
High Resolution Computed Tomography ◽  
Diffuse Lung Disease ◽  
First Choice ◽  
The Mean ◽  
The Impact ◽  
Diffuse Lung

Background: The accuracy of the number of high-resolution computed tomography (HRCT) images necessary to diagnose diffuse lung disease (DLD) is not well established. Purpose: To evaluate the impact of HRCT sampling frequency on reader confidence and accuracy for diagnosing DLD. Material and Methods: HRCT images of 100 consecutive patients with proven DLD were reviewed. They were: 48 usual interstitial pneumonia, 22 sarcoidosis, six hypersensitivity pneumonitis, five each of desquamative interstitial pneumonitis, eosinophilic granulomatosis, and lymphangioleiomyomatosis, and nine others. Inspiratory images at 1-cm increments throughout the lungs and three specified levels formed complete and limited examinations. In random order, three experts (readers 1, 2, and 3) ranked their top three diagnoses and rated confidence for their top diagnosis, independently and blinded to clinical information. Results: Using the complete versus limited examinations for correct first-choice diagnosis, accuracy for reader 1 (R1) was 81% versus 80%, respectively, for reader 2 (R2) 70% versus 70%, and for reader 3 (R3) 64% versus 59%. Reader accuracy within their top three choices for complete versus limited examinations was: R1 91% versus 91% of cases, respectively, R2 84% versus 83%, and R3 79% versus 72% of cases. No statistically significant differences were found between the diagnosis methods ( P=0.28 for first diagnosis and P=0.17 for top three choices). The confidence intervals for individual raters showed considerable overlap, and the point estimates are almost identical. The mean interreader agreement for complete versus limited HRCT for both top and top three diagnoses were the same (moderate and fair, respectively). The mean intrareader agreement between complete and limited HRCT for top and top three diagnoses were substantial and moderate, respectively. Conclusion: Overall reader accuracy and confidence in diagnosis did not significantly differ when fewer or more HRCT images were used.

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.

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