关键词: 血管分割, 脉冲耦合神经网络, 链接输入, 灰度迭代阈值, 分割性能

Abstract: Aiming at the several problems that parameters are numerous and difficult to select automatically and the ending condition of the iteration is not well determined in the traditional PCNN model, a method of blood vessels segmentation in fundus image based on gray iteration threshold PCNN is proposed. The traditional PCNN model is simplified and link input is improved into the sum of neuronal excitatory link input and inhibitory burst link input of neuronal neighborhood. Meanwhile, the exponentially decaying threshold is improved to the grayscale?iteration?threshold. When the method is applied to image segmentation, parameter or specific criteria doesn’t need to be set to determine the best number of iterations since the segmentation can be completed by one time of PCNN iterating process. The experiments implemented on the DRIVE fundus image database indicate that the method is superior to traditional PCNN method in subjective visual effect and objective segmentation performance and operation time.

Key words: vascular segmentation, pulse coupled neural network, link input, gray iteration threshold, segmentation performance