计算技术与自动化2017，Vol.36Issue(4)：141-148,8.
基于改进FCM聚类医学图像配准
Medical Image Registration Based on Improved Fuzzy C-means Clustering
摘要
Abstract
The closest iterative point (ICP) algorithm and the mutual information (MI) technology,as intensity-based and feature-based medical image registration methods respectively,are commonly put into use in medical image registration.But some naturally existing things which restrict the further development need to be faced and be solved.On the one hand,they remain heavily calculation costs and low registration efficiencies.On the other hand,since they seriously depends on whether the initial rotation and translation registration parameters can be exactly extracted,they often traps in the local optimum and even fails to register images.In this paper,we compute the centroids of the reference and floating images by using the image moments to obtain the initial translation values,and use Improved Fuzzy C-means Clustering (FCM) to classify the image coordinates.Before clustering,this proposed method first centralizes the medical image coordinates,creates the two-row coordinate matrix to construct the 2-D sample set to be partitioned into two classes,and computes the slope of a straight line fitted to the two classes,finally derives the rotation angle from solving the arc tangent of the slope and obtains the initial rotation values.Obtains through the experiment,this proposed method can efficiently avoid trapping in the local optimum and is meet the single-mode and multi-mode state image registration.It has a low computational load,a fast registration,a fairly simple implementation and good registration accuracy.关键词
图像配准/fuzzy C-means聚类/迭代最近点/互信息Key words
image registration/FCM clustering/iterative closest points/mutual information分类
信息技术与安全科学引用本文复制引用
陈园,刘军华,雷超阳..基于改进FCM聚类医学图像配准[J].计算技术与自动化,2017,36(4):141-148,8.基金项目
湖南省教育厅科研资助项目(16C720) （16C720）
