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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:
 [1];  [1];  [1];  [2];  [3]
+ Show Author Affiliations
  1. Shandong Univ. of Finance and Economics, Jinan (China). School of Computer Science and Technology; Digital Media Technology Key Lab of Shandong Province, Jinan (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Shandong Provincial Qianfoshan Hospital, Jinan (China). Respiratory Dept.
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1626225
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Computational and Mathematical Methods in Medicine
Additional Journal Information:
Journal Volume: 2015; Journal ID: ISSN 1748-670X
Publisher:
Hindawi
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; Mathematical & Computational Biology

Citation Formats

Liu, Hui, Zhang, Cai-Ming, Su, Zhi-Yuan, Wang, Kai, and Deng, Kai. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification. United States: N. p., 2015. Web. doi:10.1155/2015/185726.
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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. United States. https://doi.org/10.1155/2015/185726
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Liu, Hui, Zhang, Cai-Ming, Su, Zhi-Yuan, Wang, Kai, and Deng, Kai. Tue . "Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification". United States. https://doi.org/10.1155/2015/185726. https://www.osti.gov/servlets/purl/1626225.
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@article{osti_1626225,
title = {Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification},
author = {Liu, Hui and Zhang, Cai-Ming and Su, Zhi-Yuan and Wang, Kai and Deng, Kai},
abstractNote = {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.},
doi = {10.1155/2015/185726},
journal = {Computational and Mathematical Methods in Medicine},
number = ,
volume = 2015,
place = {United States},
year = {Tue Apr 07 00:00:00 EDT 2015},
month = {Tue Apr 07 00:00:00 EDT 2015}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1155/2015/185726
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