Correlation between high-resolution computed tomography findings and IASLC/ATS/ERS classification of small lung adenocarcinomas in Japanese patients.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. 7543-7543
Author(s):  
Yujin Kudo ◽  
Jun Matsubayashi ◽  
Yoshihisa Shimada ◽  
Soichi Akata ◽  
Yasufumi Kato ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Japanese Patients ◽  
High Resolution Computed Tomography ◽  
Lung Adenocarcinomas

scholarly journals High-resolution computed tomography classification of lung fibrosis for patients with asbestos-related disease

2001 ◽  
Vol 27 (2) ◽  
pp. 106-112 ◽  
Author(s):  
Olli Huuskonen ◽  
Leena Kivisaari ◽  
Antti Zitting ◽  
Kristian Taskinen ◽  
Antti Tossavainen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Fibrosis ◽  
High Resolution Computed Tomography ◽  
Related Disease ◽  
Asbestos Related Disease

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 Correlation between high-resolution computed tomography lung nodule characteristics and EGFR mutation in lung adenocarcinomas

2019 ◽  
Vol Volume 12 ◽  
pp. 519-526
Author(s):  
Yunqiang Nie ◽  
Hongjun Liu ◽  
Xiao Tan ◽  
Hui Wang ◽  
Fuzhou Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Egfr Mutation ◽  
Lung Nodule ◽  
High Resolution Computed Tomography ◽  
Lung Adenocarcinomas
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