Segmentation and classification of ground glass nodule on CT images

2021 ◽  
Author(s):  
Yanqiu Wang ◽  
Shihong Yue ◽  
Jun Chen ◽  
Qi Li
Keyword(s):  
Ct Images ◽  
Ground Glass ◽  
Ground Glass Nodule

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

2019 ◽  
Vol 30 (4) ◽  
pp. 1847-1855 ◽  
Author(s):  
Jing Gong ◽  
Jiyu Liu ◽  
Wen Hao ◽  
Shengdong Nie ◽  
Bin Zheng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Ct Images ◽  
Ground Glass ◽  
Learning Network ◽  
Residual Learning ◽  
Ground Glass Nodule

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

Effect of Lung Volume and Gravity on the Attenuation and Size of a Pure Ground-Glass Nodule

2012 ◽  
Vol 27 (1) ◽  
pp. W15-W17 ◽  
Author(s):  
Gilbert R. Ferretti ◽  
François Arbib ◽  
Jean François Roux ◽  
Vincent Bland ◽  
Sylvie Lantuejoul
Keyword(s):  
Lung Volume ◽  
Ground Glass ◽  
Ground Glass Nodule
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