基于群智能优化算法的医学图像分割综述  

Survey of medical image segmentation based on swarm intelligence optimization algorithm

作　　者：陈华[1] 杨帆 刘刚[1] Chen Hua;Yang Fan;Liu Gang(Key Laboratory of Measurement Technology and Instrumentation of Hebei Province,Yanshan University, Qinhuangdao 066004)

机构地区：[1]燕山大学河北省测试计量技术及仪器重点实验室,秦皇岛066004

出　　处：《高技术通讯》2019年第5期512-522,共11页Chinese High Technology Letters

基　　金：河北省自然科学基金(F2015203287)资助项目

摘　　要：医学图像是医学影像的分析基础,而分割问题是研究的关键步骤。随着智能技术的快速发展,越来越多的人将智能优化算法应用到医学图像分割中,其中群智能优化算法是当今热门的研究课题。群智能优化算法是指人类参考群体生活的昆虫、动物的复杂社会行为而提出的模拟生物系统中群体生活习性的一种新算法。本文主要介绍了包括粒子群算法(PSO)、萤火虫算法(FA)、布谷鸟搜索算法(CS)、蝙蝠算法(BA)在内的几种优化算法及其改进之后在医学图像分割等方面的应用,为学者今后的交流与研究提供参考。Medical image is the basis of medical image analysis, and segmentation is the key step of the study. With the rapid development of intelligent technology, more and more people apply intelligent optimization algorithms to medical image segmentation. Among them, swarm intelligence optimization algorithm is a hot topic. The swarm intelligence optimization algorithm refers to a new algorithm for the group living habits in the simulated biological system, which is proposed based on the complex social behavior of the insects and animals living in the human reference group. This paper mainly introduces several optimization algorithms, including particle swarm optimization (PSO), firefly worm algorithm (FA), cuckoo search (CS) and bat algorithm (BA), and their applications in medical image segmentation, which will help the scholars to communicate and study in the future.

关 键 词：医学图像分割 群智能优化 粒子群算法(PSO) 萤火虫算法(FA) 布谷鸟搜索算法(CS) 蝙蝠算法(BA) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]