| 基于改进FCM聚类医学图像配准 |
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| 引用本文：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,(4):141-148 |
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| 中文摘要:ICP和互信息广泛应用于医学图像配准，但存在以下问题：其计算量非常大，耗时长；受初始旋转和平移参数影响较大，图像配准容易造成目标函数陷入局部最优值。该方法通过计算参考图像和浮动图像的质心，获得配准平移初始值；对医学图像坐标进行中心化处理，通过改进的FCM聚类方法把图像坐标聚成2类；把这2个聚类中心拟合成一条直线，可以算出该直线的斜率，得出其倾斜角，从而获得配准旋转初始值。实验结果表明，该方法既可用于单模态图像配准，也可以用于多模态配准。还具有运算量少、图像配准速度较快、计算比较简单、精确度较高等特点，并且解决了图像配准容易陷入局部最优的问题。 |
| 中文关键词:图像配准 fuzzy C-means聚类 迭代最近点 互信息 |
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| Medical Image Registration Based on Improved Fuzzy C-means Clustering |
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| 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. |
| keywords:image registration FCM clustering iterative closest points mutual information |
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