基于脑磁共振图像配准的动态联合角点检测算法

Dynamic Combinational Corner Detection Algorithm Based on Brain Magnetic Resonance Image Registration

角点检测算法是基于角特征点的图像配准方法的核心。Harris和Susan是两种重要的角点检测算法,有较好的检测能力,但是其在描述角点信息方面都不全面。因此,联合Harris、Susan两种算法是一种较好的解决思路。其中,如何确定在联合算法中Harris、Susan两种算法的权重是一个关键。设计了一种联合算法,并通过统计实验获取两者的权重,通过引入两个加权因子ω1和ω2分别对Harris角点响应值与Susan角点响应值进行加权计算,获得其角点强度,从而筛选出新的角点集合,使该联合算法的角点检测能力明显提高。最后将该方法用于脑磁共振图像配准实验中。实验比较结果表明,该联合角点检测算法在脑磁共振图像配准的应用中,相对于目前已有角点检测算法,能获得较高的配准精度和较好的稳定性。

Corner detection algorithm is key to the image registration algorithm based on corner feature point.Harris and Susan algorithms are two important detection algorithms of them because of their satisfying detection capability.But they are not comprehensive to describe the information of the corner points.Therefore,it is good solution to combine them together to enhance their capability.For this solution,it is important to find the weight of the two algorithms.This paper proposed a combinational method,and improved its capability greatly by introducing two weighted factors ω1 and ω2 and deciding their proportion based on statistical experiments.In the end,the method was used in brain MR image registration.The experimental results show that this algorithm can be used for brain MR image registration and can obtain higher registration precision and stability compared with the existing corner detection algorithms.