Automatic lung segmentation in chest CT image using morphology

Lingma Sun; Zhenming Peng; Zhuoran Wang; Hong Pu; Lu Guo; Guohui Yuan; Fangyan Yin; Tian Pu

doi:10.1117/12.2506604

8 February 2019 Automatic lung segmentation in chest CT image using morphology

Lingma Sun, Zhenming Peng, Zhuoran Wang, Hong Pu, Lu Guo, Guohui Yuan, Fangyan Yin, Tian Pu

Proceedings Volume 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging; 108431D (2019) https://doi.org/10.1117/12.2506604
Event: Ninth International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT2018), 2018, Chengdu, China

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.

Citation Download Citation

Lingma Sun, Zhenming Peng, Zhuoran Wang, Hong Pu, Lu Guo, Guohui Yuan, Fangyan Yin, and Tian Pu "Automatic lung segmentation in chest CT image using morphology", 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); https://doi.org/10.1117/12.2506604

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