Lan, S., Liu, X., Wang, L., Cui, C. (2018) A Visually Guided Framework for Lung Segmentation and Visualization in Chest CT Images. Journal of Medical Imaging and Health Informatics, 8 (3), 485-493. doi:10.1166/jmihi.2018.2325
A visually guided framework for lung segmentation and visualization in chest CT images
|Author:||Lan, Shouren1; Liu, Xin2; Wang, Lisheng1;|
1Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
2Center for Machine Vision and Signal Analysis, University of Oulu, Finland
3Department of Vascular Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
|Online Access:||PDF Full Text (PDF, 6.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019041712650
American Scientific Publishers,
|Publish Date:|| 2019-04-17
Lung cancer is the leading cause of cancer-related death worldwide and this also stimulates the development of various computer-aided diagnosis (CAD) systems. But the conventional lung segmentation methods can’t satisfy the needs of the clinicians in lung cancer diagnosis and surgery. It is very important to provide a segmentation and visualization framework for the clinicians instead of radiologists in outpatient service. Therefore we propose a visually guided method based on a 2D feature space and spatial connectivity computation to reduce the dependence on the radiologists for lung segmentation and visualization. Our framework consists of three main processing steps. Firstly, a 2D feature space of CT scalar versus gradient magnitude is constructed. Secondly, the attribute distribution region of the lungs is selected in the 2D feature space, and then the lungs are extracted from the determined voxels by spatial connectivity computation. Finally, the lungs and pulmonary nodules are visualized simultaneously with different colors and opacities in volume rendering. Experimental results show that the proposed framework is efficient for outpatient service and can provide an intuitive segmentation process and nodules information.
Journal of medical imaging and health informatics
|Pages:||485 - 493|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the 973 program of China (No.2013CB329401), NSFC of China (No.61375020, 61572317) and Cross Research Fund of Biomedical Engineering of SJTU (No.YG2016MS55).
© 2018 American Scientific Publishing. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Journal of Medical Imaging and Health Informatics, http://dx.doi.org/10.1166/jmihi.2018.2325.