Image Segmentation Based on An Improved Pulse Coupled Neural Network for Atomic Force Microscopy

Abstract

In this study, a novel method combining pulse coupled neural network (PCNN) and social network search (SNS) is proposed to achieve accurate image segmentation for an atomic force microscopy (AFM). The proposed method utilizes the biological visual characteristics of PCNN and the solution space search ability of SNS to determine the optimal key parameters, which can address the issue of incorrect image segmentation caused by different topographic heights of specimens in an AFM image. In the tests, the performance of the proposed method is compared with the traditional PCNN method and the Otsu method, which demonstrates that the proposed method can automatically segment the AFM image with higher accuracy and robustness.