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

Authors: Maxin Wang; Xinzheng Xu; Guanying Wang; Shifei Ding; Tongfeng Sun

Addresses: School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, Jiangsu, China ' School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, Jiangsu, China ' School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, Jiangsu, China ' School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, Jiangsu, China ' School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, Jiangsu, China

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.

Keywords: pulse coupled neural network; three-dimensional model; medical image segmentation.

DOI: 10.1504/IJWMC.2017.087358

International Journal of Wireless and Mobile Computing, 2017 Vol.13 No.1, pp.72 - 77

Received: 18 Nov 2016
Accepted: 19 Apr 2017
Published online: 13 Oct 2017 *

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