Efficient Computer-Aided Detection of Ground-Glass Opacity Nodules in Thoracic CT Images

Author(s):  
Xujiong Ye ◽  
Xinyu Lin ◽  
Gareth Beddoe ◽  
Jamshid Dehmeshki
2013 ◽  
Author(s):  
Song Li ◽  
Xiabi Liu ◽  
Ali Yang ◽  
Kunpeng Pang ◽  
Chunwu Zhou ◽  
...  

2009 ◽  
Vol 56 (7) ◽  
pp. 1810-1820 ◽  
Author(s):  
Xujiong Ye ◽  
Xinyu Lin ◽  
J. Dehmeshki ◽  
G. Slabaugh ◽  
G. Beddoe

2005 ◽  
Author(s):  
Berkman Sahiner ◽  
Zhanyu Ge ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Naama Bogot ◽  
...  

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA

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.


2014 ◽  
Vol 42 (1) ◽  
pp. 144-153 ◽  
Author(s):  
Jianfei Liu ◽  
Shijun Wang ◽  
Evrim B. Turkbey ◽  
Marius George Linguraru ◽  
Jianhua Yao ◽  
...  

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