关键词: 图像分割, 脉冲耦合神经网络, 指数衰减阈值, 灰度迭代阈值, 分割性能

Abstract: A new method, called Grayscale Iteration Threshold Pulse Coupled Neural Network (GIT-PCNN), was proposed for image segmentation. The GIT-PCNN reduced the required parameters of conventional PCNN and the exponentially decaying threshold was improved to be related to the grayscale statistics of the original image. When GIT-PCNN was applied to image segmentation, no parameter or iteration time needs to be determined since the segmentation could be completed by one time of PCNN firing process. Therefore, GIT-PCNN did not require specific rule as the iteration stop condition. GIT-PCNN made good use of the grayscale information of the original image and the pulse characteristics of PCNN that the neurons associated with each group of spatially connected pixels with similar intensities tended to pulse together when partitioning images. The experimental results show that GIT-PCNN is better than classical PCNN-based segmentation algorithms on visual evaluation, subjective indices and speed performance.

Key words: image segmentation, Pulse Coupled Neural Network (PCNN), exponentially decaying threshold, grayscale iteration threshold, segmentation performance