Medical images segmentation based on improved three-dimensional pulse coupled neural network

Abstract

Pulse coupled neural network (PCNN) is the third-generation model of artificial networks, which is based on the construction of the cat visual principle. When processing the image, it has a unique advantage, and PCNN is widely used in various fields, especially in the aspect of image segmentation, image fusion, and so on. However, the traditional PCNN model has a lot of problems, such as multi-parameters, parameter setting. Moreover, exponential decay mechanism will sometimes bring certain difficulty for image segmentation, etc. To solve these problems, a simplified and improved 3D-PCNN model is proposed in this paper, through which the whole 3D brain image segmentation is achieved. The experimental results show that, the 3D-PCNN algorithm reduced the segmentation time and improved the efficiency of segmentation when compared with the traditional 2D-PCNN model, the traditional 3D-PCNN algorithm and the 3D Otsu algorithm.