The clustering effect of standard FCM and IFCM algorithm is usually just determined by membership degree in the objective function, without considering the characteristics of the data and the local spatial information, resulting in low clustering accuracy and susceptibility to noise.
Applying it to IFCM algorithm can yield a more accurate clustering result.
In our algorithm, the Euclidean intuitionistic fuzzy distance of standard IFCM is replaced by the kernel space distance metric, and the local spatial-gray information and improved intuitionistic fuzzy entropy proposed in this paper are introduced, then the novel image segmentation method based on improved IFCM algorithm is obtained, whose objective function is:
Besides the proposed method, five representative fuzzy based algorithms as FCM, IFCM, KIFCM, IFCM-S and IIFCM are evaluated as well.
2(e)-(i) shows the segmented square image by FCM , IFCM , KIFCM , IFCMS  and IIFCM  respectively with setting optimal parameter values.
3(d)-(i), corresponding to the proposed algorithm, FCM, IFCM, KIFCM, IFCM-S and IIFCM respectively.