Two-Layer Recurrent Pulse Coupled Neural Network for Image Segmentation

DOI: 10.5176/978-981-08-7656-2ATAI2010-24

Authors: Heggere S. Ranganath and Ayesha Bhatnagar

Abstract:

For over a decade, the Pulse Coupled Neural Network (PCNN) based algorithms have been used for image segmentation. Though there are several versions ofthe PCNN based image segmentation methods, almost all of them use single-layer PCNN with excitatory linking inputs. Often the PCNN parameters including the linking coefficient are determined by trial and error. This paper presents a new 2-layer network organization for PCNN in which excitatory and inhibitory linking inputs exist. The value of the linking coefficient and the threshold signal atwhich primary firing of neurons start are determined directly from the image histogram. Simulation results show that the new PCNN achieves significant improvement in the segmentation accuracy over other methods including the widely known Kuntimad’s single burst image segmentation approach. The improvement is due to the fact that neurons corresponding to spatially adjacent regions compete to capture neurons corresponding to boundary pixels. Simulation results also show that small or even moderate increase in the value of the linking coefficient from its optimal value has practically no adverse impact on the segmentation accuracy.

Keywords: Image Segmentation, Neural Networks, PulseCoupled Neural Network, Two-layer PCNN, Image Processing