In this paper, a fuzzy clustering technique for image segmentation is developed by incorporating a hybrid
of local spatial membership and data information into the conventional hard C-means (HCM) algorithm.
This incorporation is a threefold procedure. (1) The membership function of a pixel is spatially smoothed
in the pixel vicinity. (2) The Kullback-Leibler (KL) divergence between the pixel membership function and
the smoothed one is added to the HCM objective function for fuzzification. (3) The resulting fuzzified HCM
is regularized by adding a weighted HCM-like function where the original pixel data are replaced by locally
smoothed ones. Thereby the weight is proportional to the residual of the locally smoothed membership.
This residual decreases when many pixels existing in the pixel vicinity belong to the same cluster. Thus,
the weighted distance decreases, allowing the pixel membership to follow the dominant membership in
the pixel vicinity. The simulation results of segmenting synthetic, medical and media images have shown
that the proposed algorithm provides better performance compared to several previously developed
algorithms. For example, in a synthetic image, with added white Gaussian noise having a variance of 0.3,
the proposed algorithm provides accuracy, sensitivity and specificity of 92%, 84% and 94.7% respectively,
while the algorithm with the closest results provides 81.9% of accuracy, 62.2% of sensitivity and 86.8% of
specificity. In addition, the proposed algorithm shows the capability to identify the number of clusters.