Fuzzy C means (FCM) clustering is one of well known unsupervised clustering techniques

which has been widely used in automated image segmentation However

when the classical FCM algorithm is used for image segmentation

there are also some problems

such as weak robustness of distance measure

reguire ments of setting the initial number of clusters in advance

without considering local image feature In this paper

an adaptive FCM image segmentation algorithm based on the feature divergence is proposed

which can accomplish image segmentation by importing the feature divergence vector into distance measure

incorporating the cluster validity exponent to ascertain the initial number of clusters automatically and extracting the image feature according to Laws texture measure Experimental results show that the proposed method is simple and work well for most images (especially for texture images)

and has better segmentation effect than the existing FCM image segmentation.