Automated image segmentation using improved PCNN model based on cross-entropy

Pulse Coupled Neural Networks(PCNN) is a new Neural Networks which was developed and formed in the 1990's. The key point of PCNN is modulated coupled mechanism, while Coupled results produce internal activity. The output of PCNN is binary image sequence, which call be considered the results of threshold segmentation. In this paper, the matrix made by internal activity is regarded as a breadth of image, then which call be conjoined with the technique of traditional threshold segmentation. The application of minimum cross-entropy criterion in the technique of image segmentation makes the discrepancy of information content between image segmented and image after segmentation to be least. A kind of novel algorithm of image segmentation setting on cycle iterations automatically is Put forward, after traditional PCNN threshold segmentation mechanism improved with the combination OF minimum cross-entropy criterion. Theory analysis and experimental results all show that the best segmentation output can be drew from the simple and sophisticated image using this new algorithm.