[1]DI Lan,LIU Haitao,HE Ruibo.Fuzzy C-means image segmentation algorithm incorporating neighborhood information[J].CAAI Transactions on Intelligent Systems,2019,14(2):273-280.[doi:10.11992/tis.201712035]
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Fuzzy C-means image segmentation algorithm incorporating neighborhood information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 273-280 Column: 学术论文—机器学习 Public date: 2019-03-05
Title:
Fuzzy C-means image segmentation algorithm incorporating neighborhood information
Author(s):
DI Lan;  LIU Haitao;  HE Ruibo
College of Digital Media, Jiangnan University, Wuxi 214122, China
Keywords:
fuzzy C-means; image segmentation; spatial information; local information; non-local information; multidimensional model; neighborhood membership degree; penalty term
CLC:
TP391.4
DOI:
10.11992/tis.201712035
Abstract:
The fuzzy C-means algorithm (FCM) is sensitive to image noise; in addition, it only considers the image numerical information and ignores the neighborhood spatial information, resulting in inaccurate final image segmentation result. To overcome this drawback, an FCM image segmentation algorithm is proposed in which the local information and non-local information of the image are integrated into a multidimensional model, which extends the original single dimension of clustering. In addition, a prior probability is introduced into the membership matrix, so that the neighborhood information of the pixel in the membership matrix is fully considered before each iteration, and then a neighborhood membership penalty is added to correct the clustering result. Finally, a penalty of neighborhood membership degree is used to modify the clustering results. Experimental results demonstrate that the algorithm is robust against noise and achieves an ideal image segmentation effect.
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