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Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification
Joint Authors
Liu, Hui
Zhang, Cai-Ming
Su, Zhi-Yuan
Wang, Kai
Deng, Kai
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-07
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Hui& Zhang, Cai-Ming& Su, Zhi-Yuan& Wang, Kai& Deng, Kai. 2015. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1057825
Modern Language Association (MLA)
Liu, Hui…[et al.]. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1057825
American Medical Association (AMA)
Liu, Hui& Zhang, Cai-Ming& Su, Zhi-Yuan& Wang, Kai& Deng, Kai. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1057825
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057825