A Robust Medical Image Registration Algorithm Based on the SAM of Multi-Scale Harris Corners

Ying Ding, Jingtao Fan, Huamin Yang
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引用次数: 2

Abstract

To make up for the lack of concern on spatial information in conventional mutual information based image registration framework, this paper designs a novel registration algorithm based on the SAM information of multi-scale Harris corners (CSAM for short). First, the multi-scale contour is extracted, and multi-scale Harris corner detector is added to acquire the estimated transform parameters; and then CSAM is regarded as Similarity Measure function, several optimized match points are obtained, the finally registration parameters are resolved by using least squares method. This algorithm realizes registration of medical images with noise and multiresolutions, further more, it only matches corners and doesn’t need optimal searching, so it has reduced calculate time and avoided local extremum. Experimental results on clinical CT and T1-weighted MR images demonstrate that, as compared with the conventional mutual information based method, the proposed method consistently completes much higher precision, faster speed and better robustness. Keywords-image registration; square root arithmetic mean divergence(SAM); Harris corner; multi-scale
基于多尺度Harris角点SAM的鲁棒医学图像配准算法
针对传统基于互信息的图像配准框架对空间信息关注不足的问题,本文设计了一种基于多尺度Harris角点SAM信息的图像配准算法。首先提取多尺度轮廓,加入多尺度Harris角点检测器获取估计的变换参数;然后将CSAM作为相似度度量函数,得到多个优化匹配点,最后利用最小二乘法求解配准参数。该算法实现了具有噪声和多分辨率的医学图像配准,并且只匹配角点,不需要最优搜索,减少了计算时间,避免了局部极值。在临床CT和t1加权MR图像上的实验结果表明,与传统的基于互信息的方法相比,本文方法具有更高的精度、更快的速度和更好的鲁棒性。Keywords-image登记;平方根算术平均散度;哈里斯角落;多尺度
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