Region growing with pulse-coupled neural networks: an alternative to seeded region growing

Abstract

The seeded region growing (SRG) algorithm is a fast robust parameter-free method for segmenting intensity images given initial seed locations for each region. The requirement of predetermined seeds means that the model cannot operate fully autonomously. In this paper, we demonstrate a novel region growing variant of the pulse-coupled neural network (PCNN), which offers comparable performance to the SRG and is able to generate seed locations internally, opening the way to fully autonomous operation.