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Automatic lung segmentation in chest CT image using morphology
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Paper Abstract

The accuracy and efficiency of the lung segmentation are significant to computer-aided detection/diagnosis (CAD/CADx) scheme for pulmonary nodules detection in chest computed tomography (CT) image. And morphology is widely utilized to characterize the shape of the object in lung segmentation. In this investigation, a multi-stages based approach which combines thresholding, connected component analysis and morphology is proposed to achieve a fast and precise lung segmentation. The presented framework consists of three stages: thorax extraction, lung segmentation and boundary refinement. A dataset of CT scans from different equipments and modalities is utilized to evaluate the proposed method. The average dice similarity coefficient (DSC) of the experiments is 0.97 and average time-consuming of each slice is 0.64s. The results demonstrate that the proposed method with multi-stages is an efficient and accurate method for lung segmentation.

Paper Details

Date Published: 8 February 2019
PDF: 8 pages
Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108431D (8 February 2019); doi: 10.1117/12.2506604
Show Author Affiliations
Lingma Sun, Univ. of Electronic Science and Technology of China (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)
Zhuoran Wang, Univ. of Electronic Science and Technology of China (China)
Hong Pu, Sichuan Provincial People's Hospital (China)
Univ. of Electronic Science and Technology of China (China)
Lu Guo, Sichuan Provincial People's Hospital (China)
Univ. of Electronic Science and Technology of China (China)
Guohui Yuan, Univ. of Electronic Science and Technology of China (China)
Fangyan Yin, Sichuan Provincial People's Hospital (China)
Univ. of Electronic Science and Technology of China (China)
Tian Pu, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10843:
9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging
Yadong Jiang; Xiaoliang Ma; Xiong Li; Mingbo Pu; Xue Feng; Bernard Kippelen, Editor(s)

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