A segmentation of pulmonary nodules based on improved fuzzy C-means clustering algorithm

According to reports, lung cancer is gradually becoming the first cancer that threatens human life. The early stage of lung cancer is in the form of pulmonary nodules. The key issue in computer-aided diagnosis of lung tumors is to correct and accelerate rapid segmentation of diseased tissue. Therefo...

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Bibliographic Details
Published in:MATEC web of conferences Vol. 232; p. 3011
Main Authors: Yang, Tiejun, Cheng, Jinfeng, Zhu, Chunhua
Format: Journal Article Conference Proceeding
Language:English
Published: Les Ulis EDP Sciences 01.01.2018
Subjects:
Algorithms
Clustering
Domains
Lung cancer
Nodules
Pixels
Robustness (mathematics)
Segmentation
ISSN:2261-236X, 2274-7214, 2261-236X
Online Access:Get full text
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Summary:According to reports, lung cancer is gradually becoming the first cancer that threatens human life. The early stage of lung cancer is in the form of pulmonary nodules. The key issue in computer-aided diagnosis of lung tumors is to correct and accelerate rapid segmentation of diseased tissue. Therefore, this paper proposes a robust fuzzy c-mean clustering algorithm for pulmonary nodules segmentation, which can effectively improve the adaptive degree of local domain pixels. Since the information of the domain pixels does not necessarily have a positive correlation with the central pixels, the reference mechanism of domain window pixel information needs to be redefined. The robust fuzzy c-means clustering algorithm redefines the grayscale of the spatial pixel points in the domain and selects different fuzzy factors according to the reference standard. Based on this, the weights of different fuzzy factors are updated according to the characteristics of pixel points and gray fluctuation in pixel domain. The experimental results show that this method is superior to other typical algorithms in the segmentation of pulmonary nodules.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/201823203011