Unsupervised Image Segmentation Using Automated Fuzzy c-Means

Authors:
Supatra Sahaphong;Nualsawat Hiransakolwong
Affiliations:
King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand;King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
Venue:
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Year:
2007

Citing 0
Cited 3

A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Automatic histogram threshold using fuzzy measures

IEEE Transactions on Image Processing
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

An unsupervised fuzzy clustering technique, Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the conventional FCM algorithm must be estimated by expertise users to determine the cluster numbers. To overcome the limitation of FCM algorithm, an automated fuzzy c-mean (AFCM) algorithm is presented in this paper. The proposed algorithm initiates the first two centroids of clusters by a method based on Otsu algorithm and automatically determines the appropriate cluster number for image segmentation. The performance of the proposed technique has been tested with reference to conventional FCM. The experimental results demonstrate that AFCM can spontaneously estimate the appropriate number of clusters and its performance is faster convergence than the performance of the conventional FCM. Keywords : Fuzzy c-Means, Image segmentation, Clustering, Otsu algorithm.