A New Algorithm for Image Segmentation Based on Fast Fuzzy C-Means Clustering

Fuzzy c-means algorithm with spatial constraints (FCM_S) is more effective for image segmentation. However, it still lacks enough robustness to noise and outliers, and costs much time in computation. To overcome the above problem, a new algorithm for image segmentation based on fast fuzzy c-means clustering is proposed in this paper. In order to reduce the number of iteration, the algorithm selects the peak value of gray histogram as the initial centroid. To enhance the noise immunity, the clustering of centre pixel is influenced by the neighbor mean value and median value. The algorithm reduces the time of each iteration step by the gray histogram of image. The experimental results on two types of images indicate that the proposed algorithm is effective and efficient.

[1]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[2]  Xiaofeng Li,et al.  A new image segmentation algorithm based the fusion of Markov random field and fuzzy c-means clustering , 2005, IEEE International Symposium on Communications and Information Technology, 2005. ISCIT 2005..

[3]  Aly A. Farag,et al.  A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems , 1999, Pattern Recognit. Lett..

[4]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[6]  Hong Yan,et al.  An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation , 2003, IEEE Transactions on Medical Imaging.

[7]  Witold Pedrycz,et al.  Conditional Fuzzy C-Means , 1996, Pattern Recognit. Lett..

[8]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[9]  Miin Shen Yang,et al.  Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms. , 2002, Magnetic resonance imaging.

[10]  Miin-Shen Yang,et al.  Alternative c-means clustering algorithms , 2002, Pattern Recognit..

[11]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[12]  Ming Li,et al.  Fuzzy-C-Means Clustering Based On The Gray And Spatial Feature For Image Segmentation , 2006, 2006 International Conference on Computational Intelligence and Security.

[13]  A. Ben Hamza,et al.  Image denoising: a nonlinear robust statistical approach , 2001, IEEE Trans. Signal Process..

[14]  Mohammed Yakoob Siyal,et al.  An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI , 2005, Pattern Recognit. Lett..