A robust segmentation method for the AFCM-MRF model in noisy image

Authors:
Simon C. F. Tam;C. C. Leung;W. K. Tsui
Affiliations:
-;-;-
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 2
Cited 1

Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

IEEE Transactions on Fuzzy Systems

A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

A robust image segmentation algorithm based on Alternative Fuzzy C-mean clustering algorithm (AFCM) with Markov Random Field (MRF) is presented in this paper. Due to disregard of spatial constraint information, the results using Fuzzy C-Mean (FCM) and AFCM are corrupted by noise. In order to improve the robustness of noise, the spatial constraint information of an image is represented by MRF with the Gibbs function which is based on the AFCM. Comparison to the FCM, AFCM, FCM-MRF model, and the proposed algorithm has been demonstrated by the simulation and real images. Results show that AFCM-MRF model achieves better performance than other methods.