Abstract: The PSO (particle swarm optimization) and fuzzy C-Mean (FCM) algorithms were combined together to form a new algorithm and it is applied to image edge detection, thus overcoming the two shortcomings of standard FCM algorithm, i.e., sensitive to initial value and easy to fall to local minimum. The new algorithm is developed the way an eigenvector is constructed on the basis of measure theory to describe an edge point information, and each of the pixel points in a gray scale image is regarded as a data sample. The eigenvector of the information on an edge point is constructed by processing the gray level of the pixel point, and a 3-D data set is thus given. Then, the data set is classified by PSO fuzzy clustering algorithm to adaptively detect the image edge points so as to extract an edge. Simulation results showed that the new algorithm is highly antinoise and able to get better image edges with improved precision in edge positioning.