In this paper, multiple kernel fuzzy c-means is introduced as a general framework for image segmentation problem. Multiple kernel fuzzy c-means provides us a new approach to combine different information of image pixels in segmentation algorithms. That is, different information of image pixels are combined in the kernel space by combining different kernel functions defined on specific information domains. Two new segmentation algorithms are derived from the proposed framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of multiple kernel fuzzy c-means based approaches. ©2010 IEEE.