In order to improve the performance of existing image edge detection methods，a novel edge detection method on the basis of nonsubsampled contourlet transform (NSCT) and kernel fuzzy c-means clustering (KFCM) is proposed． In this method，firstly，an original image is decomposed into a low-frequency component and some high-frequency components via NSCT． Secondly，edge information is extracted from the low-frequency component with less noise and is clustered via KFCM to obtain low-frequency edge image． As a result，the accuracy of edge localization is improved． Then，in order to decrease pseudo-edges and richen image details，the method of modulus maxima is applied to high-frequency components with more edges and details． Finally，the whole image edge is obtained by fusing the edge images of low-frequency component and high-frequency components． Experimental results show that，in comparison with the Canny method，the method on the basis of edge detection operator and fuzzy clustering，the method on the basis of edge information and fuzzy c-means algorithm optimized by chaotic par-ticle swarm，as well as the method of modulus maxima in NSCT domain，the proposed method helps obtain better edge detection effect with accurate edge localization，continuous and complete edges，as well as abundant details．