基于改进PCNN和互信息熵的自动图像分割

Automated Image Segmentation Based on Modified PCNN and Mutual Information Entropy

脉冲耦合神经网络(PCNN)由于其良好的脉冲传播特性在图像分割中得到了广泛应用。针对其需要人机交互通过实验确定其相关参数等问题,改进PCNN模型,以像素对比度作为链接矩阵,以互信息作为迭代终止的判决依据,提出基于改进脉冲耦合神经网络的自动图像分割。实验结果表明,该方法实时性好、自适应性强,分割出的目标轮廓清楚。

For its good property of pulse burst,Pulse Coupled Neural Network（PCNN） is widely used in image segmentation.However,there are such problems in the method as its parameters are decided by experiment,so use the contrast of pixels as model’s link matrix,and use image mutual information entropy as the criterion to terminate iteration to modify standard model.This paper proposes an automated image segmentation based on modified PCNN.Experimental results show that the method is adaptive,its real time ability is good,and target contour is more clear.