we present an improved fuzzy

C-means (FCM) algorithm for image segmentation by introducing

a tradeoff weighted fuzzy factor and a kernel metric. The

tradeoff weighted fuzzy factor depends on the space distance of

all neighboring pixels and their gray-level difference simultaneously.

By using this factor, the new algorithm can accurately

estimate the damping extent of neighboring pixels. In order to

further enhance its robustness to noise and outliers, we introduce

a kernel distance measure to its objective function. The new

algorithm adaptively determines the kernel parameter by using

a fast bandwidth selection rule based on the distance variance

of all data points in the collection. Furthermore, the tradeoff

weighted fuzzy factor and the kernel distance measure are both

parameter free. Experimental results on synthetic and real images

show that the new algorithm is effective and efficient, and is

relatively independent of this type of noise.