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基于最小生成树的DoG关键点医学图像配准

DOI: 10.11834/jig.20110413

Keywords: 医学图像配准,DoG关键点,最小生成树,Rényi熵

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Abstract:

针对医学图像配准对鲁棒性强、准确性高和速度快的要求,提出一种基于最小生成树的DoG(differenceofGaussian)关键点配准算法。该算法首先从图像上提取DoG关键点,然后将关键点对应的灰度信息融入联合Rényi熵中,最后使用最小生成树来估计联合Rényi熵。新算法结合了DoG关键点的鲁棒性和最小生成树估计Rényi熵的高效性。实验结果表明,在图像含有噪声、灰度不均匀和初始变换范围较大的情况下,该算法在达到良好配准精度的同时,具有较强的鲁棒性和较快的速度。

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