一种基于脉冲耦合神经网络和图像熵的自动图像分割方法

Automated image segmentation using pulse coupled neural networks and images entropy

90年代发展形成的脉冲耦合神经网络(PCNN)模型特别适合于图像分割、边缘提取等方面的应用研究,但众所周知,PCNN模型图像分割效果不但取决于PCNN模型中各个参数的合理选择,而且同时还取决于循环迭代次数的确定选择准则,通常循环迭代次数N的选择通过人工交互方式来确定。正因如此选择合适的准则来确定N是PCNN图像分割的关键,但目前还没有文献提出一个合适的准则来解决这个问题。本文结合图像统计特性和PCNN参数模型提出了熵值最大准则。该准则实现了PCNN神经网络的自动图像分割。对于PCNN的理论研究和实际应用具有非常重要的现实意义。

Pulse-coupled neural network(PCNN) based on Eckhorn抯 model of the cat visual cortex find many applications in image processing, including segmentation, edge extraction et al. As all known, the performance of the image segmentation depends not only directly on the adjustment of PCNN parameters and the statistical properties of image but also on the cyclic iteration times N of PCNN. If the parameters have been properly set, it turns out to be essential to select a suitable criterion to determine N. While N is usually determined by means of visual judgement which decreases the efficient of PCNN image segmentation. This article raises a new method to implement the image segmentation automatically based on the PCNN model and the entropy of image. It is the criterion of maximal entropy of segmented binary image of PCNN output. According to this criterion, the iteration times, N, is determined automatically.