An image segmentation approach based on immune spectral clustering algorithm, is proposed, in which the dimension reduction ability of the spectral clustering is used to attain the distribution of data in the mapping space. Next, a new immune clonal clustering algorithm is proposed to cluster the sample points in the mapping space. Compact input with low-dimension for immune clonal clustering is obtained after spectral mapping, and the immune clonal clustering algorithm, characterized by its rapid convergence to global optimum and minimal sensitivity to initialization, can obtain good clustering results. To efficiently apply the algorithm to image segmentation, Nystr?m method is used to reduce the computation complexity. Experimental results on synthetic texture images and SAR images show the validity of the algorithm in image segmentation.