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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 185726, 14 pages
Research Article

Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

1School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China
2Digital Media Technology Key Lab of Shandong Province, Jinan 250014, China
3Lawrence Berkeley National Lab, University of California, Berkeley, CA 94720, USA
4Respiratory Department, Shandong Provincial Qianfoshan Hospital, Jinan 250014, China

Received 9 April 2014; Revised 1 November 2014; Accepted 21 December 2014

Academic Editor: William Crum

Copyright © 2015 Hui Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.