Abstract: A visual perception based fuzzy C-means clustering algorithm was proposed to solve the problems that the results of complex structural image segmentation are not satisfactory. Firstly, on the basis of the receptive field properties analysis of neurons in the primary visual cortex, a visual nerve cell response function was proposed to calculate image structural feature. Secondly, a ramp function was used to simulate the visual perception of relative brightness change and calculate the difference between pixels in the image and the cluster centers. The relationship in direction, relative position and periodic between neighboring stimuli and the central neuron were fully considered, so the image structural information could be accurately described with the proposed model, and the noise and the complex texture interferences could be effectively suppressed. The experimental results demonstrated that the shortcoming of traditional fuzzy C-means clustering algorithm was overcome with the proposed algorithm, and accurate segmentation of complex background images was achieved.