Image Segmentation using Two-Layer Pulse Coupled Neural Network with Inhibitory Linking Field

Abstract

For over a decade, the Pulse Coupled Neural Network
(PCNN) based algorithms have been used for image
segmentation. Though there are several versions of the PCNN
based image segmentation methods, almost all of them use singlelayer
PCNN with excitatory linking inputs. There are four
major issues associated with the single-burst PCNN which need
attention. Often, the PCNN parameters including the linking
coefficient are determined by trial and error. The segmentation
accuracy of the single-layer PCNN is highly sensitive to the value
of the linking coefficient. Finally, in the single-burst mode,
neurons corresponding to background pixels do not participate
in the segmentation process. This paper presents a new 2-layer
network organization of PCNN in which excitatory and
inhibitory linking inputs exist. The value of the linking
coefficient and the threshold signal at which primary firing of
neurons start are determined directly from the image statistics.
Simulation results show that the new PCNN achieves significant
improvement in the segmentation accuracy over the widely
known Kuntimad’s single burst image segmentation approach.
The two-layer PCNN based image segmentation method
overcomes all three drawbacks of the single-layer PCNN.