Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network

The pulse-coupled neural network (PCNN) is widely used in image segmentation. However, the determination of parameter values in the PCNN framework is an unavoidable and trivial task that may cause neurons to behave unexpectedly, thus affecting segmentation performance. Therefore, this paper presents an efficient iterative algorithm using a modified PCNN for automatic image segmentation. In contrast to existing PCNN models, a new neural threshold was first established for the modified PCNN instead of a general dynamic threshold, allowing for greater efficiency in controlling the pulse output. Besides, a varying linking coefficient value was constructed for efficiently adjusting the neural behavior. By incorporating the Bayes clustering method, it thereby extends the feasibility of the model for the extraction of targets with inhomogeneous brightness, thus resulting in a simpler iterative algorithm for segmentation. Experiments on real-world infrared images demonstrate the efficiency of our proposed model. Moreover, compared with simplified PCNN models and classic segmentation methods, the proposed model shows fewer misclassification errors and higher segmentation performance.