title: Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.
abstract: The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.
authors.affiliation: School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China ; Digital Media Technology Key Lab of Shandong Province, Jinan 250014, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China ; Digital Media Technology Key Lab of Shandong Province, Jinan 250014, China.
authors.forename: Cai-Ming
authors.initials: CM
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China ; Digital Media Technology Key Lab of Shandong Province, Jinan 250014, China.
authors.forename: Zhi-Yuan
authors.initials: ZY
authors.lastname: Su
authors.affiliation: Lawrence Berkeley National Lab, University of California, Berkeley, CA 94720, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Respiratory Department, Shandong Provincial Qianfoshan Hospital, Jinan 250014, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D055088:Early Detection of Cancer / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000098:blood supply; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed