title: Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images.
abstract: Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
authors.forename: Zhenghao
authors.identifier: 0000-0001-6123-2276
authors.initials: Z
authors.lastname: Shi
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
authors.forename: Jiejue
authors.identifier: 0000-0003-0860-6114
authors.initials: J
authors.lastname: Ma
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
authors.forename: Minghua
authors.initials: M
authors.lastname: Zhao
authors.affiliation: Xianyang Hospital, Yan'an University, Xianyang 712000, China.
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
authors.forename: Yaning
authors.initials: Y
authors.lastname: Feng
authors.affiliation: First Affiliated Hospital of School of Medicine, Xian Jiaotong University, Xian 710061, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Zhang
authors.affiliation: School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi 480-1198, Japan.
authors.forename: Lifeng
authors.initials: L
authors.lastname: He
authors.affiliation: Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616-3793, USA.
authors.forename: Kenji
authors.initials: K
authors.lastname: Suzuki
mesh_terms: D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*