Spatial improved fuzzy c-means clustering for image segmentation

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a new modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, GFCM, similar to FCM, is sensitive to noise in normal gray...

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Bibliographic Details
Published in:2011 International Conference on Electronic and Mechanical Engineering and Information Technology Vol. 9; pp. 4791 - 4794
Main Authors: Feng Zhao, Licheng Jiao
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2011
Subjects:
Clustering algorithms
Fuzzy clustering algorithm
Image segmentation
Noise
Noise measurement
Partitioning algorithms
Robustness
Spatial information
Speckle
Synthetic aperture radar (SAR) image
ISBN:9781612840871, 1612840876
Online Access:Get full text
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Summary:The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a new modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, GFCM, similar to FCM, is sensitive to noise in normal gray-level images. In order to overcome this problem, a novel fuzzy segmentation algorithm called spatial improved fuzzy c-means clustering algorithm (IFCM_S) is proposed in this paper. In IFCM_S, a spatial constraint term is introduced into the objective function of GFCM, and the center and membership function update equations are also presented. Experiments on synthetic and synthetic aperture radar (SAR) images, show that the proposed method behaves well in segmentation performance and speed.
ISBN:9781612840871
1612840876
DOI:10.1109/EMEIT.2011.6024110