A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance than traditional clustering based approaches on segmenting noised synthetic and real images.