Fuzzy c-means clustering with spatial information for image segmentation

A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.

[1]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[3]  M. Brandt,et al.  Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. , 1994, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[4]  M.C. Clark,et al.  MRI segmentation using fuzzy clustering techniques , 1994, IEEE Engineering in Medicine and Biology Magazine.

[5]  Witold Pedrycz,et al.  Fuzzy clustering with partial supervision , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[6]  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.

[7]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[8]  Yadong Wang,et al.  Improving fuzzy c-means clustering based on feature-weight learning , 2004, Pattern Recognit. Lett..

[9]  Y. Fukuyama,et al.  A new method of choosing the number of clusters for the fuzzy c-mean method , 1989 .

[10]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[11]  J. Bezdek.  Cluster Validity with Fuzzy Sets , 1973 .

[12]  Abraham Kandel,et al.  Feature-based fuzzy classification for interpretation of mammograms , 2000, Fuzzy Sets Syst..