Paper Info
Title
Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory
Abstract
Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.
Year
DOI
Venue
2008
10.1093/ietfec/e91-a.3.723
IEICE Transactions
Keywords
Field
DocType
image segmentation,markov random field,image analysis application,fuzzy clustering,spatial constraint,proposed algorithm,novel image segmentation algorithm,segment image,spatial constraint information,fcm algorithm,real-world image,bayesian theory,membership function,image analysis,spatial information
Spatial analysis,Fuzzy clustering,Pattern recognition,Markov random field,Fuzzy logic,Robustness (computer science),Image segmentation,Artificial intelligence,Cluster analysis,Mathematics,Bayesian probability
Journal
Volume
Issue
ISSN
E91-A
3
0916-8508
Citations 
PageRank 
References 
9
0.71
12
Authors
3
Name
Order
Citations
PageRank
Xiaohe Li1272.96
Taiyi Zhang217617.60
Zhan Qu3201.83