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3 results on '"juxtapleural nodules"'

1. An Automated Segmentation Method for Lung Parenchyma Image Sequences Based on Fractal Geometry and Convex Hull Algorithm.

Author

Xiao, Xiaojiao, Zhao, Juanjuan, Qiang, Yan, Wang, Hua, Xiao, Yingze, Zhang, Xiaolong, and Zhang, Yudong

Subjects
PULMONARY nodules, IMAGE segmentation, ITERATIVE methods (Mathematics)
Abstract

Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently. [ABSTRACT FROM AUTHOR]

Published
2018
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2. An Automated Segmentation Method for Lung Parenchyma Image Sequences Based on Fractal Geometry and Convex Hull Algorithm

Author

Xiaojiao Xiao, Juanjuan Zhao, Yan Qiang, Hua Wang, Yingze Xiao, Xiaolong Zhang, and Yudong Zhang

Subjects
fractal geometry, convex hull method, juxtapleural nodules, lung parenchymal segmentation, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently.

Published
2018
Full Text
View/download PDF

3. An Automated Segmentation Method for Lung Parenchyma Image Sequences Based on Fractal Geometry and Convex Hull Algorithm

Author

Xiaolong Zhang, Wang Hua, Juanjuan Zhao, Yan Qiang, Yingze Xiao, Xiaojiao Xiao, and Yudong Zhang

Subjects
Convex hull, fractal geometry, convex hull method, juxtapleural nodules, lung parenchymal segmentation, Computer science, Iterative method, Automated segmentation, 02 engineering and technology, lcsh:Technology, 030218 nuclear medicine & medical imaging, Image (mathematics), lcsh:Chemistry, 03 medical and health sciences, 0302 clinical medicine, Fractal, Parenchyma, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Segmentation, Instrumentation, lcsh:QH301-705.5, Fluid Flow and Transfer Processes, Basis (linear algebra), business.industry, lcsh:T, Process Chemistry and Technology, General Engineering, Pattern recognition, respiratory system, lcsh:QC1-999, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 020201 artificial intelligence & image processing, Artificial intelligence, business, lcsh:Engineering (General). Civil engineering (General), lcsh:Physics
Abstract

Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently.

Published
2018

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