为进一步提高现有图像边缘检测方法的性能，提出了一种基于非下采样 Contourlet变换(NSCT)和核模糊 c-均值(KFCM)聚类的图像边缘检测方法． 首先通过 NSCT 将原始图像分解成低频分量和高频分量;然后对含噪声较少的低频分量提取边缘信息，并采用 KFCM聚类算法进行聚类得到低频边缘图像，以提高定位精度，而对于边缘细节信息较多的高频分量各个子带，通过模极大值检测边缘以减少伪边缘，丰富图像细节;最后对低频和高频图像边缘进行融合得到完整的边缘． 实验结果表明，相比于 Canny 方法、边缘检测算子与模糊聚类结合的方法、边缘信息与混沌粒子群优化的模糊聚类结合的方法、NSCT 域模极大值方法，文中方法具有更好的边缘检测效果，边缘定位准确、完整、连续、细节丰富．

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．