An Improved Non-coupled PCNN Model for Image Segmentation

Abstract

Pulse-coupled neural network (PCNN) model is widely used in digital image processing, but it is always a difficult problem to set network parameters and determine the optimal segmentation. By analyzing the firing characteristics and network parameters setting for the non-coupled linking PCNN, we propose an improved non-coupled linking PCNN for image segmentation. The model introduce the coupling effect of neighboring neurons into the dynamic threshold subsystem, and using a combination of DNN network, manual adjusting on step length for setting the dynamic threshold initial value. When the dynamic threshold initial value is adjusted properly, the optimal segmentation for the image can be obtained. Using the proposed algorithm in image segmentation of Lena and mammographic images, the segmentation effect similar to that of the traditional model can be obtained by less iteration, and it shows faster speed and better robustness.